METHODS, SYSTEMS, ARTICLES OF MANUFACTURE AND APPARATUS TO CATEGORIZE IMAGE TEXT

Abstract

Methods, apparatus, systems, and articles of manufacture are disclosed to categorize image text. An example apparatus includes region detection model training circuitry to identify candidate regions in an input image that include text, and generate bounding boxes around respective ones of the identified candidate regions. The example apparatus also includes mask application circuitry to improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes, and OCR circuitry to perform OCR on the masked input image to obtain text data within the bounding boxes.

Description

RELATED APPLICATION

This patent arises from U.S. Provisional Patent Application Ser. No. 63/053,505, which was filed on Jul. 17, 2020. U.S. Provisional Patent Application No. 63/053,505 is hereby incorporated herein by reference in its entirety. Priority to U.S. Provisional Patent Application No. 63/053,505 is hereby claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to computer-based image analysis and, more particularly, to methods, systems, articles of manufacture and apparatus to categorize image text.

BACKGROUND

Image recognition involves computer-aided techniques to analyze pictures or photographs to determine and/or identify the content of the captured scene (e.g., the recognition of the general subject matter of the scene and/or the recognition of individual objects within the scene). Such techniques are useful in different applications across different industries. For example, retail establishments, product manufacturers, and other business establishments may take advantage of image recognition techniques of photographs of such establishments (e.g., pictures of product shelving) to identify quantities and/or types of products in inventory, to identify shelves that need to be restocked and/or the frequency with which products need restocking, to recognize and read product barcodes or textual information about the product, to assess product arrangements and displays, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of an example system for banner categorization to categorize image text in a manner consistent with this disclosure.

FIG. 2 is a schematic illustration of text classification circuitry of FIG. 1.

FIG. 3 is an example training image including an example banner and corresponding banner images.

FIG. 4 is a schematic illustration of an example framework to categorize image text.

FIG. 5 is a schematic illustration of another example framework to categorize image text.

FIG. 6 is a schematic illustration of another example framework to categorize image text.

FIGS. 7A and 7B are example banners.

FIG. 7C is an example banner.

FIG. 8 is a flowchart flowcharts representative of example machine readable instructions that may be executed by example processor circuitry to implement the example system of FIG. 1, the example text classification circuitry of FIGS. 1 and 2, and/or the example framework of FIGS. 4, 5 and 6.

FIG. 9 is a flowchart representative of example machine readable instructions that may be executed by example processor circuitry to process an image.

FIG. 10 is a block diagram of an example processing platform including processor circuitry structured to execute the example machine readable instructions of FIGS. 8 and 9 to implement the example system of FIG. 1, the example text classification circuitry of FIGS. 1 and 2, and/or the example framework of FIGS. 4, 5 and 6.

FIG. 11 is a block diagram of an example implementation of the processor circuitry of FIG. 10.

FIG. 12 is a block diagram of another example implementation of the processor circuitry of FIG. 10.

FIG. 13 is a block diagram of an example software distribution platform (e.g., one or more servers) to distribute software (e.g., software corresponding to the example machine readable instructions of FIGS. 8 and 9) to client devices associated with end users and/or consumers (e.g., for license, sale, and/or use), retailers (e.g., for sale, re-sale, license, and/or sub-license), and/or original equipment manufacturers (OEMs) (e.g., for inclusion in products to be distributed to, for example, retailers and/or to other end users such as direct buy customers).

The figures are not to scale. Instead, the thickness of the layers or regions may be enlarged in the drawings. Although the figures show layers and regions with clean lines and boundaries, some or all of these lines and/or boundaries may be idealized. In reality, the boundaries and/or lines may be unobservable, blended, and/or irregular. In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. As used herein, unless otherwise stated, the term “above” describes the relationship of two parts relative to Earth. A first part is above a second part, if the second part has at least one part between Earth and the first part. Likewise, as used herein, a first part is “below” a second part when the first part is closer to the Earth than the second part. As noted above, a first part can be above or below a second part with one or more of: other parts therebetween, without other parts therebetween, with the first and second parts touching, or without the first and second parts being in direct contact with one another.

Unless specifically stated otherwise, descriptors such as “first,” “second,” “third,” etc., are used herein without imputing or otherwise indicating any meaning of priority, physical order, arrangement in a list, and/or ordering in any way, but are merely used as labels and/or arbitrary names to distinguish elements for ease of understanding the disclosed examples. In some examples, the descriptor “first” may be used to refer to an element in the detailed description, while the same element may be referred to in a claim with a different descriptor such as “second” or “third.” In such instances, it should be understood that such descriptors are used merely for identifying those elements distinctly that might, for example, otherwise share a same name. As used herein, “approximately” and “about” refer to dimensions that may not be exact due to manufacturing tolerances and/or other real world imperfections. As used herein “substantially real time” refers to occurrence in a near instantaneous manner recognizing there may be real world delays for computing time, transmission, etc. Thus, unless otherwise specified, “substantially real time” refers to real time +/−1 second. As used herein, the phrase “in communication,” including variations thereof, encompasses direct communication and/or indirect communication through one or more intermediary components, and does not require direct physical (e.g., wired) communication and/or constant communication, but rather additionally includes selective communication at periodic intervals, scheduled intervals, aperiodic intervals, and/or one-time events. As used herein, “processor circuitry” is defined to include (i) one or more special purpose electrical circuits structured to perform specific operation(s) and including one or more semiconductor-based logic devices (e.g., electrical hardware implemented by one or more transistors), and/or (ii) one or more general purpose semiconductor-based electrical circuits programmed with instructions to perform specific operations and including one or more semiconductor-based logic devices (e.g., electrical hardware implemented by one or more transistors). Examples of processor circuitry include programmed microprocessors, Field Programmable Gate Arrays (FPGAs) that may instantiate instructions, Central Processor Units (CPUs), Graphics Processor Units (GPUs), Digital Signal Processors (DSPs), XPUs, or microcontrollers and integrated circuits such as Application Specific Integrated Circuits (ASICs). For example, an XPU may be implemented by a heterogeneous computing system including multiple types of processor circuitry (e.g., one or more FPGAs, one or more CPUs, one or more GPUs, one or more DSPs, etc., and/or a combination thereof) and application programming interface(s) (API(s)) that may assign computing task(s) to whichever one(s) of the multiple types of the processing circuitry is/are best suited to execute the computing task(s).

DETAILED DESCRIPTION

A banner image is a portion of an image that contains product representations (e.g., an image portion) and associated textual descriptions. A banner, as distinguished from a banner image, is a collection of two or more banner images on a page/image. For example, a banner may be an advertisement column in, for example a store newspaper, which the banner image contains several banner images (e.g., portions of the page/image having advertisements for different products) in similar or various categories, along with their associated textual descriptions (e.g., a product name, a package size, etc.). In some examples, a banner may contain hundreds of product banner images (e.g., a buy one get one free offer, a sale promotion, etc.) for varying products.

With this, it may be advantageous to classify the banner images in a banner using a coding technique. As used herein, coding or otherwise a coding technique refers to a method of analyzing an input image (e.g., a banner image) to determine and/or otherwise annotate categories of each element in the banner (e.g., input image). For example, a coding technique may be implemented on a banner to determine the categories of each advertisement of the corresponding banner images. Further in such an example, a coding technique may produce results identifying which advertisements are skin-care advertisements, which advertisements are home improvement advertisements, which advertisements are car part advertisements, etc. With this, various entities may be able to gain insight into product organization, product placement, advertisement type, product category, etc.

To improve coding techniques, examples disclosed herein employ Artificial Intelligence (AI) to efficiently perform a task otherwise not feasible with the human mind. Examples disclosed herein utilize Computer Vision (CV) and Natural Language Processing (NLP) techniques to efficiently perform a coding technique on an input image. In particular, the vast number of products, product types, product differences between retailers, and the quantity of a given product in a given store introduce challenges for analyzing and coding such images as such requiring a high degree of temporal sequencing. Examples disclosed herein improve automation at which banner categorization is performed to reduce human error in analysis and image annotating increase the quality of image coding, and reduce time associated with image coding and categorization.

Examples disclosed herein utilize a neural network such as, for example, a region-based convolutional neural network (R-CNN) to detect the regions where the text of descriptions is located. For example, an R-CNN may be employed to determine regions of text likely to be a description of an associated product in a banner image. In response, examples disclosed herein utilize Optical Character Recognition (OCR) to examine a banner image pixel by pixel, looking for shapes that match the character traits. As such, examples disclosed herein apply an OCR-based algorithm over the regions previously detected using an R-CNN to obtain the text of interest related to the descriptions of the products in a banner image. In other examples, a Faster R-CNN, or a Region Proposal Network (RPN) may be utilized in place of an R-CNN. The examples, disclosed herein employ R-CNN neural networks to train models adapted to handle polysemy, and nuance enhancements.

In response to identifying, determining, and/or otherwise obtaining the text recognized from the interest regions, examples disclosed herein classify the text to determine the category of text to perform image (e.g., banner) categorization. In some examples, classification of the recognized and obtained text identifies one or more product categories (e.g., an ingredient of a product, a product type, a nutritional fact, a product size, a package size, an address, etc.).

FIG. 1 is a schematic illustration of an example system for banner categorization. As shown in FIG. 1, the system 100 may include a first image 102A, a second image 102B, and/or and third image 102C for categorization. In some examples, the images 102A, 102B, and/or 102C are banner images, whereas in other examples the images 102A, 102B, and/or 102C are a different type of image. In some examples, each image 102A-102C includes one or more product representations (e.g., an image, a graphic depicting the product, etc.), respectively. For example, image 102A includes product representation(s) 104A, image 102B includes product representation(s) 104B, and image 102C includes product representation(s) 104C, respectively. In some examples, each image 102A-102C includes one or more portions of text (e.g., a product description, a product ingredient listing, a promotional sale, a package size, etc.), respectively. For example, image 102A includes text 106A, image 102B includes text 106B, and image 102C includes text 106C, respectively. In some examples, each image is uploaded directly to an example network 108 for processing.

As shown by the system 100, an example categorization circuitry 110 may be used to perform the categorization of the images 102A-102C including respective portions of text 106A-106C. In some examples, the categorization circuitry 110 includes an example image database 112, an example text classification circuitry 114, and an example creditor 116. The image database 112, for example, may store some or a set of images 102A-102C uploaded to the network 108. For example, if at a particular time only images uploaded on a particular time or date are to be coded and analyzed, the image database 112 may store those images. In some examples, the text classification circuitry 114, can analyze the images stored in the image database 114 and perform categorization. The results of the text categorization for the one or more images may then be sent to the creditor 116. In some examples, the creditor 116 generates an example report 118 to report the text categorizations of the images analyzed by the text classification circuitry 114 of the categorization circuitry 110.

FIG. 2 illustrated the example text classification circuitry 114 as shown in FIG. 1. In some examples, the text classification circuitry 114 includes an example region detection model training circuitry 202 for detecting one or more regions of an image 102A-102C related to text or textual descriptions of each product. R-CNN model architectures utilize Ground-Truth (GT) information that is known to be real or true. For example, in the examples disclosed herein, GT information is employed about generated bounding boxes and associated classes of information to teach one or more neural networks to localize and classify objects of interest. In particular, once bounding boxes are localized in a particular image, the R-CNN classifies the bounding boxes based on the GT information provided. As such, the region detection model training circuitry 202 utilizes a set of one or more GT training images 204 stored in an example database 203 of the text classification circuitry 114. The training image(s) 204 may include images of example image banners including one or more product representations (shown in FIG. 1) and or text (shown in FIG. 1) associated with the product representation. In some examples, the region detection model training circuitry 202 utilizes a set of one or more bounding box annotation(s) 206 to further train the R-CNN to recognize text within generated bounding boxes circumscribing portions containing text within an image.

In some examples, the text classification circuitry 114 includes one or more databases 203. The example database 203 may be implemented as any type of storage device and include training images 204, bounding box annotations 206, detected training region(s) 207, detected training text 216, detected regions 220, detected text 222, category descriptions 226, category numbers 228, analysis results 230, accuracy results 232, precision results 234 and/or recall results 236. The aforementioned example database 203 elements may be data structures with numeric, alphabetic and/or alphanumeric information contained therein.

FIG. 3 is an example training image 300 including example banner images 302, 304, and 306 for use with the region detection model training circuitry 202 of FIG. 2. While three (3) example banner images are singled-out or otherwise specified in FIG. 3, several more banner images are located in the example training image 300 and not discussed further. As shown by FIG. 3, the banner images 302, 304, and 306 include identified GT bounding boxes 308, 310, and 312, around text for training purposes. Once the training image(s) 204 are labelled with the appropriate bounding boxes, the R-CNN model is trained by inputting the training images 204 jointly with the bounding box annotation(s) 206. As such, in some examples, the region detection model training circuitry 202 utilizes the training image(s) 204 and the bounding box annotation(s) 206 to generate a trained region detection model 208. In some examples, one or more models (e.g., the example region detection model 208) may be stored in the example database 203 as a data structure. In some examples, a Region Proposal Network (RPN) is applied to propose multiple objects identifiable within a particular image.

In some examples, the text classification circuitry 114 includes a text classification training circuitry 210 to train a second R-CNN neural network to recognize text in the region detected containing textual descriptions. In some examples, the text classification training circuitry 210 utilizes the training image(s) 204, the bounding box annotation(s) 206, and an example set of training categories (e.g., a data structure of stored training categories) to generate a model for classifying text recognized by the example region detection model 208. In some examples, the text classification training circuitry 210 applies a technique based on optical character recognition (OCR) to recognize the text associated with the previously detected product descriptions. The OCR technique converts the images 102A-102C (shown in FIG. 1) into machine readable text data. Typically, the human visual system reads and interprets text by recognizing the patterns of contrast between light and dark, translating those patterns into characters and words, and then attaching meaning to the characters and words recognized. Similarly, OCR attempts to mimic this visual technique by using neural networks. As such, the neural network architecture applied by the text classification training circuitry 210 applies OCR techniques to return characters and words obtained from images and respective locations. In some examples, example mask application circuitry 212 applies a mask to increase contrast (e.g., blacken) areas of an image that are not contained in any bounding boxes detected by the region detection model 208. In some examples, the darkened regions do not interfere with the regions of interest related to product descriptions during application of one or more OCR techniques/processes. Accordingly, example disclosed herein improve computational efficiency and accuracy by application of masks prior to application of OCR techniques. In some examples, example OCR circuitry 214 recognizes the text in each of the bounding boxes of the training image(s) 204. In some examples, the text classification training circuitry 210 processes the text to reduce blurring, and/or errors associated with unrecognized symbols, errors derived from lower/upper case letters or dictionary-based corrections. After recognizing the text corresponding to product descriptions in images, the example text classification training circuitry 210 applies a text classification algorithm to predict the different product categories of interest. In some examples, each product is associated to more than one category. The illustrated example of FIG. 2 also includes a text classification model 218 and category identification circuitry 224.

Returning back to FIG. 3, the example banner images 302, 304, and 306 (e.g., within corresponding regions of a rectangular shape in the image 300 having individual product information therein) further include number identifiers 314, 316 and 318 added by a business coding team, for example, to denote a category associated with a description in the training image(s) 204 for model training purposes. Additionally, the illustrated example of FIG. 3 includes banner text 308, 310 and 312. Banner text is distinguished from text that might be present on images of the various products within each region.

In operation, the example region detection model training circuitry 202 applies one or more trained region detection models to an input image, such as the example banner image 300 of FIG. 3. Generally speaking, examples disclosed herein detect regions related to textual descriptions of each product in a banner image, then recognize associated banner text as distinguished from any other text that might reside on the product images themselves, and further classifies the recognized text into different product categories, as described in further detail below. Because a banner includes at least two or more regions in which each region contains a banner image and corresponding banner text, the example region detection model training circuitry 202 detects such candidate regions in the banner. Typically, candidate regions have an orthogonal shape that is square or rectangular, but other shapes may be realized by examples disclosed herein.

The example region detection model training circuitry 202 employs an R-CNN schema to differentiate and/or otherwise distinguish banner text from text that may reside on product images within the candidate regions. In particular, the R-CNN schema differentiates the particular appearance of text found on product images versus text found within the candidate region so that banner text (e.g., text written by an advertiser of the banner) is differentiated from the rest of the text in the region of interest.

In some examples, the region detection model training circuitry 202 generates bounding boxes around detected candidate regions and/or around banner text. Briefly returning to the illustrated example of FIG. 3, the region detection model training circuitry 202 generates the bounding boxes 308, 310 and/or 312 to distinguish and/or otherwise identify banner text as distinguished from the text that may reside on the adjacent products within each banner image. In some examples, the region detection model training circuitry 202 employs R-CNN architectures with ground truth information corresponding to bounding boxes and associated classes so that the network is trained to localize and classify one or more objects of interest. When performing banner categorization, some examples include labeling of the bounding boxes, as shown in FIG. 3. In particular, example labelling in FIG. 3 is shown as bolded numbers (e.g., “82,” “84,” “96,” etc.). Such numbers correspond to coding efforts that may be unique to particular advertisers and/or entities chartered with the responsibility of categorizing banners. Such codes/labels permit knowledge of categories associated with particular descriptions, which is particularly helpful as training set data.

Upon completion of labeling of the training set, the R-CNN model is trained by inputting images with ground truth in connection with hyperparameters. Once trained, the model is applied to perform predictions of bounding boxes over unknown banner images.

As described above, performing OCR facilitates text recognition, which is particularly helpful when attempting to identify and/or otherwise detect products and corresponding product descriptions printed on a banner. While attempts to perform OCR on detected and/or otherwise predicted bounding boxes will eventually yield meaning of text, the OCR techniques can become hindered by other text near such bounding boxes and/or other text that might be printed on the products themselves. Such artifacts cause error and performance/speed degradation in the OCR process. Examples disclosed herein reduce such errors and improve OCR performance.

The example mask application circuitry 212 applies a mask to the candidate bounding boxes and/or candidate regions of the banner. Turning briefly to FIG. 7A, an example banner is shown with four (4) separate banner images (regions). In some examples, the illustrated example of FIG. 7C is generated by examples disclosed herein to generate bounding boxes on the example banner. The example mask application circuitry 212 applies the example mask in such a manner as to occlude, block, mask and/or otherwise obscure all portions of the banner except those corresponding to the predicted bounding boxes, as shown in FIG. 7B.

With the text available on the banner with reduced and/or otherwise eliminated interference from other text unassociated with the banner text, the example OCR circuitry 214 performs OCR on the banner in a manner with improved speed, reduced computational demand and reduced error. In some examples, the OCR circuitry 214 post-processes the text to reduce typical well-known errors in NLP techniques, such as those errors associated with symbols incorrectly detected, errors derived from lower/upper case letters, dictionary-based corrections, etc.

The example OCR circuitry 214 obtains text corresponding to the contents of the banner text bounding boxes and the example text classification circuitry 114 applies one or more trained text classification models in an effort to classify the text into one or more categories. In particular, the example category identification circuitry 224 classifies the text into one or more categories. Each product is associated with at least one category, but in some examples a product is associated with two or more categories, as needed. The example text classification model employed and/or otherwise invoked by the category identification circuitry 224 is a machine learning approach that employs a neural network having at least one layer. In some examples, the neural network structure/architecture generates a bag-of-words representation of the text, where embeddings are fetched for each word. In some examples, the embeddings are averaged to obtain a single embedding for the whole text in a hidden layer of the architecture. Once the averaged embeddings are computed, the single vector is fed to a linear classifier that applies softmax. In some examples, character n-grams are applied, which are helpful for classification that typically contains one or several descriptive words and/or typographical errors corresponding to the OCR technique(s).

For training such a text classification architecture, examples disclosed herein construct at least one dataset with manually labeled data corresponding to categories of interest associated with each text description. The trained model is used to perform inference of the categories related to each product description, and the inference output gives a vector with probabilities for each available category of interest. In some examples, a threshold is set corresponding to evaluation results and used to filter the categories corresponding to each detected instance.

FIGS. 4, 5 and 6 are schematic diagrams of an example framework to categorize text from banners. In particular, the example framework is implemented by the illustrated examples of FIGS. 1 and 2, in which FIG. 4 is a framework 400 corresponding to training models for region detection, FIG. 5 is a framework 500 corresponds to training models for text classification, and FIG. 6 is a framework 600 corresponds to banner categorization inference. In the illustrated example of FIG. 4, the framework 400 takes as inputs training images 204 and ground truth bounding box annotations 206, which are provided to the example region detection model training circuitry 202. Iterations of this framework 400 generate any number of trained region detection model(s) 208, which are provided to the example runtime framework 600 of FIG. 6, discussed in further detail below.

In the illustrated example of FIG. 5, the framework 500 takes as input training images 204, ground truth bounding box annotations 206 and ground truth category descriptions 226. As described above, the example framework 500 of FIG. 5 addresses traditional bottleneck issues corresponding to performing OCR techniques by invoking the example mask application circuitry 212 prior to invoking the example OCR circuitry 214 to generate detected text(s) 222. Additionally, the example framework 500 invokes the example text classification training circuitry 210 to take as further input the ground truth category descriptions 226 and generate the example trained text classification model 218, which is applied during runtime.

In the illustrated example of FIG. 6, the framework 600 takes as input new (runtime) image data 602 and applies the previously generated trained region detection model 208 from FIG. 4 (e.g., a first model). New detected regions 220 are identified and the example mask circuitry 212 is applied in advance of the example OCR circuitry 214 to generate detected texts 222. A second model 618 (e.g., the text classification model 218 from FIG. 5) is then invoked to generate identified categories 606.

In some examples, the region detection model training circuitry 202 includes means for region detection model training, the example text classification training circuitry 210 includes means for text classification training, the example mask application circuitry 212 includes means for masking, the example OCR circuitry 214 includes means for OCRing, and the example category identification circuitry 224 includes means for category identifying. For example, the means for region detection model training may be implemented by the example region detection model training circuitry 202, the means for text classification training may be implemented by the example text classification training circuitry 210, the means for masking may be implemented by the example mask application circuitry 212, the means for OCRing may be implemented by the example OCR circuitry 214, and the means for category identifying may be implemented by the example category identification circuitry 224. In some examples, the aforementioned circuitry may be implemented by machine executable instructions such as that implemented by at least blocks of FIGS. 8 and/or 9 executed by processor circuitry, which may be implemented by the example processor circuitry 1012 of FIG. 10, the example processor circuitry 1100 of FIG. 11, and/or the example Field Programmable Gate Array (FPGA) circuitry 1200 of FIG. 12. In other examples, the aforementioned circuitry is implemented by other hardware logic circuitry, hardware implemented state machines, and/or any other combination of hardware, software, and/or firmware. For example, the aforementioned circuitry may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an Application Specific Integrated Circuit (ASIC), a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate.

While an example manner of implementing the text classification circuitry 114 of FIG. 1 is illustrated in FIGS. 2, 4, 5 and 6, one or more of the elements, processes, and/or devices illustrated in FIGS. 1, 2, 4, 5 and 6 may be combined, divided, re-arranged, omitted, eliminated, and/or implemented in any other way. Further, the example region detection model training circuitry 202, the example text classification training circuitry 210, the example mask application circuitry 212, the example OCR circuitry 214, the example category identification circuitry 224, and/or, more generally, the example text classification circuitry 114 of FIGS. 1 and 2 may be implemented by hardware, software, firmware, and/or any combination of hardware, software, and/or firmware. Thus, for example, any of the example region detection model training circuitry 202, the example text classification training circuitry 210, the example mask application circuitry 212, the example OCR circuitry 214, the example category identification circuitry 224, and/or, more generally, the example text classification circuitry 114 of FIGS. 1 and 2, could be implemented by processor circuitry, analog circuit(s), digital circuit(s), logic circuit(s), programmable processor(s), programmable microcontroller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), and/or field programmable logic device(s) (FPLD(s)) such as Field Programmable Gate Arrays (FPGAs). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example region detection model training circuitry 202, the example text classification training circuitry 210, the example mask application circuitry 212, the example OCR circuitry 214, the example category identification circuitry 224, and/or, more generally, the example text classification circuitry 114 of FIGS. 1 and 2 is/are hereby expressly defined to include a non-transitory computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc., including the software and/or firmware. Further still, the example text classification circuitry 114 of FIGS. 1 and/or 2 may include one or more elements, processes, and/or devices in addition to, or instead of, those illustrated in FIGS. 1 and/or 2, and/or may include more than one of any or all of the illustrated elements, processes and devices.

Flowcharts representative of example hardware logic circuitry, machine readable instructions, hardware implemented state machines, and/or any combination thereof for implementing the text classification circuitry 114 of FIGS. 1 and 2 are shown in FIGS. 8 and 9. The machine readable instructions may be one or more executable programs or portion(s) of an executable program for execution by processor circuitry, such as the processor circuitry 1012 shown in the example processor platform 1000 discussed below in connection with FIG. 10 and/or the example processor circuitry discussed below in connection with FIGS. 11 and/or 12. The program(s) may be embodied in software stored on one or more non-transitory computer readable storage media such as a CD, a floppy disk, a hard disk drive (HDD), a DVD, a Blu-ray disk, a volatile memory (e.g., Random Access Memory (RAM) of any type, etc.), or a non-volatile memory (e.g., FLASH memory, an HDD, etc.) associated with processor circuitry located in one or more hardware devices, but the entire program and/or parts thereof could alternatively be executed by one or more hardware devices other than the processor circuitry and/or embodied in firmware or dedicated hardware. The machine readable instructions may be distributed across multiple hardware devices and/or executed by two or more hardware devices (e.g., a server and a client hardware device). For example, the client hardware device may be implemented by an endpoint client hardware device (e.g., a hardware device associated with a user) or an intermediate client hardware device (e.g., a radio access network (RAN) gateway that may facilitate communication between a server and an endpoint client hardware device). Similarly, the non-transitory computer readable storage media may include one or more mediums located in one or more hardware devices. Further, although the example program(s) is/are described with reference to the flowcharts illustrated in FIGS. 8 and 9, many other methods of implementing the example text classification circuitry 114 may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. Additionally or alternatively, any or all of the blocks may be implemented by one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware. The processor circuitry may be distributed in different network locations and/or local to one or more hardware devices (e.g., a single-core processor (e.g., a single core central processor unit (CPU)), a multi-core processor (e.g., a multi-core CPU), etc.) in a single machine, multiple processors distributed across multiple servers of a server rack, multiple processors distributed across one or more server racks, a CPU and/or a FPGA located in the same package (e.g., the same integrated circuit (IC) package or in two or more separate housings, etc).

The machine readable instructions described herein may be stored in one or more of a compressed format, an encrypted format, a fragmented format, a compiled format, an executable format, a packaged format, etc. Machine readable instructions as described herein may be stored as data or a data structure (e.g., as portions of instructions, code, representations of code, etc.) that may be utilized to create, manufacture, and/or produce machine executable instructions. For example, the machine readable instructions may be fragmented and stored on one or more storage devices and/or computing devices (e.g., servers) located at the same or different locations of a network or collection of networks (e.g., in the cloud, in edge devices, etc.). The machine readable instructions may require one or more of installation, modification, adaptation, updating, combining, supplementing, configuring, decryption, decompression, unpacking, distribution, reassignment, compilation, etc., in order to make them directly readable, interpretable, and/or executable by a computing device and/or other machine. For example, the machine readable instructions may be stored in multiple parts, which are individually compressed, encrypted, and/or stored on separate computing devices, wherein the parts when decrypted, decompressed, and/or combined form a set of machine executable instructions that implement one or more operations that may together form a program such as that described herein.

In another example, the machine readable instructions may be stored in a state in which they may be read by processor circuitry, but require addition of a library (e.g., a dynamic link library (DLL)), a software development kit (SDK), an application programming interface (API), etc., in order to execute the machine readable instructions on a particular computing device or other device. In another example, the machine readable instructions may need to be configured (e.g., settings stored, data input, network addresses recorded, etc.) before the machine readable instructions and/or the corresponding program(s) can be executed in whole or in part. Thus, machine readable media, as used herein, may include machine readable instructions and/or program(s) regardless of the particular format or state of the machine readable instructions and/or program(s) when stored or otherwise at rest or in transit.

The machine readable instructions described herein can be represented by any past, present, or future instruction language, scripting language, programming language, etc. For example, the machine readable instructions may be represented using any of the following languages: C, C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language (HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example operations of FIGS. 8 and 9 may be implemented using executable instructions (e.g., computer and/or machine readable instructions) stored on one or more non-transitory computer and/or machine readable media such as optical storage devices, magnetic storage devices, an HDD, a flash memory, a read-only memory (ROM), a CD, a DVD, a cache, a RAM of any type, a register, and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the terms non-transitory computer readable medium and non-transitory computer readable storage medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are used herein to be open ended terms. Thus, whenever a claim employs any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, having, etc.) as a preamble or within a claim recitation of any kind, it is to be understood that additional elements, terms, etc., may be present without falling outside the scope of the corresponding claim or recitation. As used herein, when the phrase “at least” is used as the transition term in, for example, a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. The term “and/or” when used, for example, in a form such as A, B, and/or C refers to any combination or subset of A, B, C such as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) B with C, or (7) A with B and with C. As used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. As used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B.

As used herein, singular references (e.g., “a”, “an”, “first”, “second”, etc.) do not exclude a plurality. The term “a” or “an” object, as used herein, refers to one or more of that object. The terms “a” (or “an”), “one or more”, and “at least one” are used interchangeably herein. Furthermore, although individually listed, a plurality of means, elements or method actions may be implemented by, e.g., the same entity or object. Additionally, although individual features may be included in different examples or claims, these may possibly be combined, and the inclusion in different examples or claims does not imply that a combination of features is not feasible and/or advantageous.

In the illustrated example of FIG. 8, the example text classification circuitry 114 invokes the example region detection model training circuitry 202 to generate and/or otherwise train the region detection model(s) (block 802). The example text classification circuitry 114 invokes the example text classification training circuitry 210 to generate and/or otherwise train the text classification model(s) (block 804). As described above, model training includes training images and ground truth data. The example text classification circuitry 114 then proceeds to process images during a runtime stage (block 806), and the example category identification circuitry 224 classifies text into one or more categories (block 808).

FIG. 9 illustrates additional detail corresponding to processing the image (block 806) of FIG. 8. In the illustrated example of FIG. 9, the example region detection model training circuitry 202 applies a trained region detection model (block 904) and proceeds to detect candidate region(s) in the input image that include text (block 906). The example region detection model training circuitry 202 generates bounding boxes around detected regions (block 908), and the example mask application circuitry 212 applies a mask to the candidate region of the image (block 910). The example OCR circuitry 214 applies OCR techniques to the masked image (block 912) and obtains detected text (block 914). The example text classification circuitry 114 applies the trained text classification model (block 916) for the purpose of classifying text into one or more categories (block 918). In the event the example region detection model training circuitry 202 determines that there are additional/subsequent regions to process (block 920), control returns to block 910, otherwise the example text classification circuitry 114 determines whether there is additional/subsequent text to be processed (block 922). If so, control returns to block 916, otherwise the example process ends.

FIG. 10 is a block diagram of an example processor platform 1000 structured to execute and/or instantiate the machine readable instructions and/or operations of FIGS. 8 and/or 9 to implement the text classification circuitry 114 of FIGS. 1 and/or 2. The processor platform 1000 can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), an Internet appliance, a gaming console, a personal video recorder, a set top box, a headset (e.g., an augmented reality (AR) headset, a virtual reality (VR) headset, etc.) or other wearable device, or any other type of computing device.

The processor platform 1000 of the illustrated example includes processor circuitry 1012. The processor circuitry 1012 of the illustrated example is hardware. For example, the processor circuitry 1012 can be implemented by one or more integrated circuits, logic circuits, FPGAs microprocessors, CPUs, GPUs, DSPs, and/or microcontrollers from any desired family or manufacturer. The processor circuitry 1012 may be implemented by one or more semiconductor based (e.g., silicon based) devices. In this example, the processor circuitry 412 implements the example region detection model training circuitry 202, the example text classification training circuitry 210, the example mask application circuitry 212, the example OCR circuitry 214, the example category identification circuitry 224, and/or, more generally, the example text classification circuitry 114 of FIGS. 1 and 2.

The processor circuitry 1012 of the illustrated example includes a local memory 1013 (e.g., a cache, registers, etc.). The processor circuitry 1012 of the illustrated example is in communication with a main memory including a volatile memory 1014 and a non-volatile memory 1016 by a bus 1018. The volatile memory 1014 may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory (RDRAM®), and/or any other type of RAM device. The non-volatile memory 1016 may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory 1014, 1016 of the illustrated example is controlled by a memory controller 1017.

The processor platform 1000 of the illustrated example also includes interface circuitry 1020. The interface circuitry 1020 may be implemented by hardware in accordance with any type of interface standard, such as an Ethernet interface, a universal serial bus (USB) interface, a Bluetooth® interface, a near field communication (NFC) interface, a PCI interface, and/or a PCIe interface.

In the illustrated example, one or more input devices 1022 are connected to the interface circuitry 1020. The input device(s) 1022 permit(s) a user to enter data and/or commands into the processor circuitry 1012. The input device(s) 422 can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, an isopoint device, and/or a voice recognition system.

One or more output devices 1024 are also connected to the interface circuitry 1020 of the illustrated example. The output devices 1024 can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube (CRT) display, an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer, and/or speaker. The interface circuitry 1020 of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip, and/or graphics processor circuitry such as a GPU.

The interface circuitry 1020 of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) by a network 1026. The communication can be by, for example, an Ethernet connection, a digital subscriber line (DSL) connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, an optical connection, etc.

The processor platform 1000 of the illustrated example also includes one or more mass storage devices 1028 to store software and/or data. Examples of such mass storage devices 1028 include magnetic storage devices, optical storage devices, floppy disk drives, HDDs, CDs, Blu-ray disk drives, redundant array of independent disks (RAID) systems, solid state storage devices such as flash memory devices, and DVD drives.

The machine executable instructions 1032, which may be implemented by the machine readable instructions of FIGS. 8 and/or 9, may be stored in the mass storage device 1028, in the volatile memory 1014, in the non-volatile memory 1016, and/or on a removable non-transitory computer readable storage medium such as a CD or DVD.

FIG. 11 is a block diagram of an example implementation of the processor circuitry 1012 of FIG. 10. In this example, the processor circuitry 1012 of FIG. 10 is implemented by a microprocessor 1100. For example, the microprocessor 1100 may implement multi-core hardware circuitry such as a CPU, a DSP, a GPU, an XPU, etc. Although it may include any number of example cores 1102 (e.g., 1 core), the microprocessor 1100 of this example is a multi-core semiconductor device including N cores. The cores 1102 of the microprocessor 1100 may operate independently or may cooperate to execute machine readable instructions. For example, machine code corresponding to a firmware program, an embedded software program, or a software program may be executed by one of the cores 1102 or may be executed by multiple ones of the cores 1102 at the same or different times. In some examples, the machine code corresponding to the firmware program, the embedded software program, or the software program is split into threads and executed in parallel by two or more of the cores 1102. The software program may correspond to a portion or all of the machine readable instructions and/or operations represented by the flowcharts of FIGS. 8 and 9.

The cores 1102 may communicate by an example bus 1104. In some examples, the bus 1104 may implement a communication bus to effectuate communication associated with one(s) of the cores 1102. For example, the bus 1104 may implement at least one of an Inter-Integrated Circuit (I2C) bus, a Serial Peripheral Interface (SPI) bus, a PCI bus, or a PCIe bus. Additionally or alternatively, the bus 1104 may implement any other type of computing or electrical bus. The cores 1102 may obtain data, instructions, and/or signals from one or more external devices by example interface circuitry 1106. The cores 1102 may output data, instructions, and/or signals to the one or more external devices by the interface circuitry 1106. Although the cores 1102 of this example include example local memory 1120 (e.g., Level 1 (L1) cache that may be split into an L1 data cache and an L1 instruction cache), the microprocessor 1100 also includes example shared memory 1110 that may be shared by the cores (e.g., Level 2 (L2 cache)) for high-speed access to data and/or instructions. Data and/or instructions may be transferred (e.g., shared) by writing to and/or reading from the shared memory 1110. The local memory 1120 of each of the cores 1102 and the shared memory 1110 may be part of a hierarchy of storage devices including multiple levels of cache memory and the main memory (e.g., the main memory 1014, 1016 of FIG. 10). Typically, higher levels of memory in the hierarchy exhibit lower access time and have smaller storage capacity than lower levels of memory. Changes in the various levels of the cache hierarchy are managed (e.g., coordinated) by a cache coherency policy.

Each core 1102 may be referred to as a CPU, DSP, GPU, etc., or any other type of hardware circuitry. Each core 1102 includes control unit circuitry 1114, arithmetic and logic (AL) circuitry (sometimes referred to as an ALU) 1116, a plurality of registers 1118, the L1 cache 1120, and an example bus 1122. Other structures may be present. For example, each core 1102 may include vector unit circuitry, single instruction multiple data (SIMD) unit circuitry, load/store unit (LSU) circuitry, branch/jump unit circuitry, floating-point unit (FPU) circuitry, etc. The control unit circuitry 1114 includes semiconductor-based circuits structured to control (e.g., coordinate) data movement within the corresponding core 1102. The AL circuitry 1116 includes semiconductor-based circuits structured to perform one or more mathematic and/or logic operations on the data within the corresponding core 1102. The AL circuitry 1116 of some examples performs integer based operations. In other examples, the AL circuitry 1116 also performs floating point operations. In yet other examples, the AL circuitry 1116 may include first AL circuitry that performs integer based operations and second AL circuitry that performs floating point operations. In some examples, the AL circuitry 1116 may be referred to as an Arithmetic Logic Unit (ALU). The registers 1118 are semiconductor-based structures to store data and/or instructions such as results of one or more of the operations performed by the AL circuitry 1116 of the corresponding core 1102. For example, the registers 1118 may include vector register(s), SIMD register(s), general purpose register(s), flag register(s), segment register(s), machine specific register(s), instruction pointer register(s), control register(s), debug register(s), memory management register(s), machine check register(s), etc. The registers 1118 may be arranged in a bank as shown in FIG. 11. Alternatively, the registers 1118 may be organized in any other arrangement, format, or structure including distributed throughout the core 1102 to shorten access time. The bus 1120 may implement at least one of an I2C bus, a SPI bus, a PCI bus, or a PCIe bus

Each core 1102 and/or, more generally, the microprocessor 1100 may include additional and/or alternate structures to those shown and described above. For example, one or more clock circuits, one or more power supplies, one or more power gates, one or more cache home agents (CHAs), one or more converged/common mesh stops (CMSs), one or more shifters (e.g., barrel shifter(s)) and/or other circuitry may be present. The microprocessor 1100 is a semiconductor device fabricated to include many transistors interconnected to implement the structures described above in one or more integrated circuits (ICs) contained in one or more packages. The processor circuitry may include and/or cooperate with one or more accelerators. In some examples, accelerators are implemented by logic circuitry to perform certain tasks more quickly and/or efficiently than can be done by a general purpose processor. Examples of accelerators include ASICs and FPGAs such as those discussed herein. A GPU or other programmable device can also be an accelerator. Accelerators may be on-board the processor circuitry, in the same chip package as the processor circuitry and/or in one or more separate packages from the processor circuitry.

FIG. 12 is a block diagram of another example implementation of the processor circuitry 1012 of FIG. 10. In this example, the processor circuitry 1012 is implemented by FPGA circuitry 1200. The FPGA circuitry 1200 can be used, for example, to perform operations that could otherwise be performed by the example microprocessor 1100 of FIG. 11 executing corresponding machine readable instructions. However, once configured, the FPGA circuitry 1200 instantiates the machine readable instructions in hardware and, thus, can often execute the operations faster than they could be performed by a general purpose microprocessor executing the corresponding software.

More specifically, in contrast to the microprocessor 1100 of FIG. 11 described above (which is a general purpose device that may be programmed to execute some or all of the machine readable instructions represented by the flowcharts of FIGS. 8 and 9 but whose interconnections and logic circuitry are fixed once fabricated), the FPGA circuitry 1200 of the example of FIG. 12 includes interconnections and logic circuitry that may be configured and/or interconnected in different ways after fabrication to instantiate, for example, some or all of the machine readable instructions represented by the flowcharts of FIGS. 8 and 9. In particular, the FPGA 1200 may be thought of as an array of logic gates, interconnections, and switches. The switches can be programmed to change how the logic gates are interconnected by the interconnections, effectively forming one or more dedicated logic circuits (unless and until the FPGA circuitry 1200 is reprogrammed). The configured logic circuits enable the logic gates to cooperate in different ways to perform different operations on data received by input circuitry. Those operations may correspond to some or all of the software represented by the flowcharts of FIGS. 8 and 9. As such, the FPGA circuitry 1200 may be structured to effectively instantiate some or all of the machine readable instructions of the flowcharts of FIGS. 8 and 9 as dedicated logic circuits to perform the operations corresponding to those software instructions in a dedicated manner analogous to an ASIC. Therefore, the FPGA circuitry 1200 may perform the operations corresponding to the some or all of the machine readable instructions of FIGS. 8 and 9 faster than the general purpose microprocessor can execute the same.

In the example of FIG. 12, the FPGA circuitry 1200 is structured to be programmed (and/or reprogrammed one or more times) by an end user by a hardware description language (HDL) such as Verilog. The FPGA circuitry 1200 of FIG. 12, includes example input/output (I/O) circuitry 1202 to obtain and/or output data to/from example configuration circuitry 1204 and/or external hardware (e.g., external hardware circuitry) 1206. For example, the configuration circuitry 1204 may implement interface circuitry that may obtain machine readable instructions to configure the FPGA circuitry 1200, or portion(s) thereof. In some such examples, the configuration circuitry 1204 may obtain the machine readable instructions from a user, a machine (e.g., hardware circuitry (e.g., programmed or dedicated circuitry) that may implement an Artificial Intelligence/Machine Learning (AI/ML) model to generate the instructions), etc. In some examples, the external hardware 1206 may implement the microprocessor 1100 of FIG. 11. The FPGA circuitry 1200 also includes an array of example logic gate circuitry 1208, a plurality of example configurable interconnections 1210, and example storage circuitry 1212. The logic gate circuitry 1208 and interconnections 1210 are configurable to instantiate one or more operations that may correspond to at least some of the machine readable instructions of FIGS. 8 and 9 and/or other desired operations. The logic gate circuitry 1208 shown in FIG. 12 is fabricated in groups or blocks. Each block includes semiconductor-based electrical structures that may be configured into logic circuits. In some examples, the electrical structures include logic gates (e.g., And gates, Or gates, Nor gates, etc.) that provide basic building blocks for logic circuits. Electrically controllable switches (e.g., transistors) are present within each of the logic gate circuitry 1208 to enable configuration of the electrical structures and/or the logic gates to form circuits to perform desired operations. The logic gate circuitry 1208 may include other electrical structures such as look-up tables (LUTs), registers (e.g., flip-flops or latches), multiplexers, etc.

The interconnections 1210 of the illustrated example are conductive pathways, traces, vias, or the like that may include electrically controllable switches (e.g., transistors) whose state can be changed by programming (e.g., using an HDL instruction language) to activate or deactivate one or more connections between one or more of the logic gate circuitry 1208 to program desired logic circuits.

The storage circuitry 1212 of the illustrated example is structured to store result(s) of the one or more of the operations performed by corresponding logic gates. The storage circuitry 1212 may be implemented by registers or the like. In the illustrated example, the storage circuitry 1212 is distributed amongst the logic gate circuitry 1208 to facilitate access and increase execution speed.

The example FPGA circuitry 1200 of FIG. 12 also includes example Dedicated Operations Circuitry 1214. In this example, the Dedicated Operations Circuitry 1214 includes special purpose circuitry 1216 that may be invoked to implement commonly used functions to avoid the need to program those functions in the field. Examples of such special purpose circuitry 1216 include memory (e.g., DRAM) controller circuitry, PCIe controller circuitry, clock circuitry, transceiver circuitry, memory, and multiplier-accumulator circuitry. Other types of special purpose circuitry may be present. In some examples, the FPGA circuitry 1200 may also include example general purpose programmable circuitry 1218 such as an example CPU 1220 and/or an example DSP 1222. Other general purpose programmable circuitry 1218 may additionally or alternatively be present such as a GPU, an XPU, etc., that can be programmed to perform other operations.

Although FIGS. 11 and 12 illustrate two example implementations of the processor circuitry 1012 of FIG. 10, many other approaches are contemplated. For example, as mentioned above, modern FPGA circuitry may include an on-board CPU, such as one or more of the example CPU 1220 of FIG. 12. Therefore, the processor circuitry 1012 of FIG. 10 may additionally be implemented by combining the example microprocessor 1100 of FIG. 11 and the example FPGA circuitry 1200 of FIG. 12. In some such hybrid examples, a first portion of the machine readable instructions represented by the flowcharts of FIGS. 8 and 9 may be executed by one or more of the cores 1102 of FIG. 11 and a second portion of the machine readable instructions represented by the flowcharts of FIGS. 8 and 9 may be executed by the FPGA circuitry 1200 of FIG. 12.

In some examples, the processor circuitry 1012 of FIG. 10 may be in one or more packages. For example, the processor circuitry 1100 of FIG. 11 and/or the FPGA circuitry 1200 of FIG. 12 may be in one or more packages. In some examples, an XPU may be implemented by the processor circuitry 1012 of FIG. 10, which may be in one or more packages. For example, the XPU may include a CPU in one package, a DSP in another package, a GPU in yet another package, and an FPGA in still yet another package.

A block diagram illustrating an example software distribution platform 1305 to distribute software such as the example machine readable instructions 1032 of FIG. 10 to hardware devices owned and/or operated by third parties is illustrated in FIG. 13. The example software distribution platform 1305 may be implemented by any computer server, data facility, cloud service, etc., capable of storing and transmitting software to other computing devices. The third parties may be customers of the entity owning and/or operating the software distribution platform 1305. For example, the entity that owns and/or operates the software distribution platform 1305 may be a developer, a seller, and/or a licensor of software such as the example machine readable instructions 1032 of FIG. 10. The third parties may be consumers, users, retailers, OEMs, etc., who purchase and/or license the software for use and/or re-sale and/or sub-licensing. In the illustrated example, the software distribution platform 1305 includes one or more servers and one or more storage devices. The storage devices store the machine readable instructions 1032, which may correspond to the example machine readable instructions of FIGS. 8 and 9, as described above. The one or more servers of the example software distribution platform 1305 are in communication with a network 1310, which may correspond to any one or more of the Internet and/or any of the example networks described above. In some examples, the one or more servers are responsive to requests to transmit the software to a requesting party as part of a commercial transaction. Payment for the delivery, sale, and/or license of the software may be handled by the one or more servers of the software distribution platform and/or by a third party payment entity. The servers enable purchasers and/or licensors to download the machine readable instructions 1032 from the software distribution platform 1305. For example, the software, which may correspond to the example machine readable instructions of FIGS. 8 and 9, may be downloaded to the example processor platform 1000, which is to execute the machine readable instructions 1032 to implement the text classification circuitry 114. In some example, one or more servers of the software distribution platform 1305 periodically offer, transmit, and/or force updates to the software (e.g., the example machine readable instructions 1032 of FIG. 10) to ensure improvements, patches, updates, etc., are distributed and applied to the software at the end user devices.

From the foregoing, it will be appreciated that example systems, methods, apparatus, and articles of manufacture have been disclosed that improve an efficient utilization of OCR techniques, decrease computational (e.g., processor) demand, reduce errors, and generally improve the efficiency of entities chartered with the responsibility of categorizing banners. The disclosed systems, methods, apparatus, and articles of manufacture are accordingly directed to one or more improvement(s) in the operation of a machine such as a computer or other electronic and/or mechanical device.

Example methods, apparatus, systems, and articles of manufacture to categorize image text are disclosed herein. Further examples and combinations thereof include the following:

Example 1 includes an apparatus comprising at least one of a central processing unit, a graphic processing unit or a digital signal processor, the at least one of the central processing unit, the graphic processing unit or the digital signal processor having control circuitry to control data movement within the processor circuitry, arithmetic and logic circuitry to perform one or more first operations corresponding to instructions, and one or more registers to store a result of the one or more first operations, the instructions in the apparatus, a Field Programmable Gate Array (FPGA), the FPGA including logic gate circuitry, a plurality of configurable interconnections, and storage circuitry, the logic gate circuitry and interconnections to perform one or more second operations, the storage circuitry to store a result of the one or more second operations, or Application Specific Integrate Circuitry including logic gate circuitry to perform one or more third operations, the processor circuitry to at least one of perform at least one of the first operations, the second operations or the third operations to identify candidate regions in an input image that includes text, generate bounding boxes around respective ones of the identified candidate regions, improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes, and perform OCR on the masked input image to obtain text data within the boundary boxes.

Example 2 includes the apparatus as defined in example 1, wherein the input image is a banner.

Example 3 includes the apparatus as defined in example 2, wherein the banner includes a plurality of banner images.

Example 4 includes the apparatus as defined in example 1, wherein the processor circuitry is to classify the obtained text data.

Example 5 includes the apparatus as defined in example 1, wherein the processor circuitry is to categorize the obtained text data based on coded images corresponding to respective ones of the candidate regions.

Example 6 includes the apparatus as defined in example 1, wherein the processor circuitry is to distinguish banner text from product label text.

Example 7 includes At least one non-transitory computer readable storage medium comprising instructions that, when executed, cause at least one processor to at least identify candidate regions in an input image that includes text, generate bounding boxes around respective ones of the identified candidate regions, improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes, and perform OCR on the masked input image to obtain text data within the boundary boxes.

Example 8 includes the at least one computer readable storage medium as defined in example 7, wherein the instructions, when executed, cause the at least one processor to identify the input image as a banner.

Example 9 includes the at least one computer readable storage medium as defined in example 8, wherein the instructions, when executed, cause the at least one processor to identify a plurality of banner images in the banner.

Example 10 includes the at least one computer readable storage medium as defined in example 7, wherein the instructions, when executed, cause the at least one processor to classify the obtained text data.

Example 11 includes the at least one computer readable storage medium as defined in example 7, wherein the instructions, when executed, cause the at least one processor to categorize the obtained text data based on coded images corresponding to respective ones of the candidate regions.

Example 12 includes the at least one computer readable storage medium as defined in example 7, wherein the instructions, when executed, cause the at least one processor to distinguish banner text from product label text.

Example 13 includes a method comprising identifying, by executing an instruction with at least one processor, candidate regions in an input image that includes text, generating, by executing an instruction with the at least one processor, bounding boxes around respective ones of the identified candidate regions, improving optical character recognition (OCR) by applying, by executing an instruction with the at least one processor, a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes, and performing, by executing an instruction with the at least one processor, OCR on the masked input image to obtain text data within the boundary boxes.

Example 14 includes the method as defined in example 13, wherein the input image is a banner.

Example 15 includes the method as defined in example 14, wherein the banner includes a plurality of banner images.

Example 16 includes the method as defined in example 13, further including classifying the obtained text data.

Example 17 includes an apparatus to identify text comprising region detection model training circuitry to identify candidate regions in an input image that include text, and generate bounding boxes around respective ones of the identified candidate regions, mask application circuitry to improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes, and OCR circuitry to perform OCR on the masked input image to obtain text data within the bounding boxes.

Example 18 includes the apparatus as defined in example 17, further including category identification circuitry to classify the obtained text data.

Example 19 includes the apparatus as defined in example 17, further including text classification circuitry to categorize the obtained text data based on coded images corresponding to respective ones of the candidate regions.

Example 20 includes the apparatus as defined in example 17, wherein the region model training circuitry is to distinguish banner text from product label text.

Although certain example systems, methods, apparatus, and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all systems, methods, apparatus, and articles of manufacture fairly falling within the scope of the claims of this patent.

The following claims are hereby incorporated into this Detailed Description by this reference, with each claim standing on its own as a separate embodiment of the present disclosure.

Claims

1. An apparatus comprising:

at least one of a central processing unit, a graphic processing unit or a digital signal processor, the at least one of the central processing unit, the graphic processing unit or the digital signal processor having control circuitry to control data movement within the processor circuitry, arithmetic and logic circuitry to perform one or more first operations corresponding to instructions, and one or more registers to store a result of the one or more first operations, the instructions in the apparatus;

a Field Programmable Gate Array (FPGA), the FPGA including logic gate circuitry, a plurality of configurable interconnections, and storage circuitry, the logic gate circuitry and interconnections to perform one or more second operations, the storage circuitry to store a result of the one or more second operations; or

Application Specific Integrate Circuitry including logic gate circuitry to perform one or more third operations;

the processor circuitry to at least one of perform at least one of the first operations, the second operations or the third operations to:

identify candidate regions in an input image that includes text;

generate bounding boxes around respective ones of the identified candidate regions;

improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes; and

perform OCR on the masked input image to obtain text data within the boundary boxes.

2. The apparatus as defined in claim 1, wherein the input image is a banner.

3. The apparatus as defined in claim 2, wherein the banner includes a plurality of banner images.

4. The apparatus as defined in claim 1, wherein the processor circuitry is to classify the obtained text data.

5. The apparatus as defined in claim 1, wherein the processor circuitry is to categorize the obtained text data based on coded images corresponding to respective ones of the candidate regions.

6. The apparatus as defined in claim 1, wherein the processor circuitry is to distinguish banner text from product label text.

7. At least one non-transitory computer readable storage medium comprising instructions that, when executed, cause at least one processor to at least:

identify candidate regions in an input image that includes text;

generate bounding boxes around respective ones of the identified candidate regions;

improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes; and

perform OCR on the masked input image to obtain text data within the boundary boxes.

8. The at least one computer readable storage medium as defined in claim 7, wherein the instructions, when executed, cause the at least one processor to identify the input image as a banner.

9. The at least one computer readable storage medium as defined in claim 8, wherein the instructions, when executed, cause the at least one processor to identify a plurality of banner images in the banner.

10. The at least one computer readable storage medium as defined in claim 7, wherein the instructions, when executed, cause the at least one processor to classify the obtained text data.

11. The at least one computer readable storage medium as defined in claim 7, wherein the instructions, when executed, cause the at least one processor to categorize the obtained text data based on coded images corresponding to respective ones of the candidate regions.

12. The at least one computer readable storage medium as defined in claim 7, wherein the instructions, when executed, cause the at least one processor to distinguish banner text from product label text.

13. A method comprising:

identifying, by executing an instruction with at least one processor, candidate regions in an input image that includes text;

generating, by executing an instruction with the at least one processor, bounding boxes around respective ones of the identified candidate regions;

improving optical character recognition (OCR) by applying, by executing an instruction with the at least one processor, a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes; and

performing, by executing an instruction with the at least one processor, OCR on the masked input image to obtain text data within the boundary boxes.

14. The method as defined in claim 13, wherein the input image is a banner.

15. The method as defined in claim 14, wherein the banner includes a plurality of banner images.

16. The method as defined in claim 13, further including classifying the obtained text data.

17. An apparatus to identify text comprising:

region detection model training circuitry to: identify candidate regions in an input image that include text; and generate bounding boxes around respective ones of the identified candidate regions;

mask application circuitry to improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes; and

OCR circuitry to perform OCR on the masked input image to obtain text data within the bounding boxes.

18. The apparatus as defined in claim 17, further including category identification circuitry to classify the obtained text data.

19. The apparatus as defined in claim 17, further including text classification circuitry to categorize the obtained text data based on coded images corresponding to respective ones of the candidate regions.

20. The apparatus as defined in claim 17, wherein the region model training circuitry is to distinguish banner text from product label text.