Abstract: A first binary image is generated by binarizing an input image based on a threshold, a second binary image is generated by changing a pixel that has predetermined high luminance in the input image into a black pixel, and whether a black pixel cluster in the second binary image is made to be an extraction target is determined based on a position of a character image identified based on a black pixel cluster in the first binary image, and a position of the black pixel cluster in the second binary image.

Abstract: A system may include a computer readable medium and a processor communicatively coupled to the computer readable medium. The processor may be configured to: obtain a graphical image file, the graphical image file including an image, wherein the image includes at least one sequence of repeating pattern elements, each of the at least one sequence including the repeating pattern elements that are repeated along a linear direction; and convert the graphical image file to at least one file including hardware directives that when executed cause a recreation of the image of the graphical image file to be drawn, wherein a file size of the at least one file is smaller than the graphical image file.

Abstract: Computer-implemented methods and systems for colour-based image tagging and colour-based searching. The method may include identifying, using image analysis, one or more dominant colours of a product based on an image of the product and receiving selection of at least one of the one or more dominant colours. In response to receiving the selection of the at least one of the one or more dominant colours, a search for products matching the at least one of the one or more dominant colours may be initiated to obtain one or more results of the searching, the one or more results including at least one product matching the at least one of the one or more dominant colours.

Abstract: Disclosed herein is a document image analysis apparatus including: a document image acquisition unit configured to acquire a document image; a region detection unit configured to detect a plurality of regions from the document image acquired by the document image acquisition unit; a clustering unit configured to cluster the plurality of regions detected by the region detection unit to integrate into a cluster; and a reading order assignment unit configured to assign a reading order to a plurality of regions belonging to the cluster within the cluster integrated by the clustering unit.

Abstract: A method and apparatus for recognizing an imaged information-bearing medium, a computer-readable storage device and a computer device are provided. The method comprising: acquiring a first image of the imaged information-bearing medium; performing text recognition on the first image to acquire a text content of the imaged information-bearing medium; classifying the imaged information-bearing medium to acquire a type of the imaged information-bearing medium; and archiving the text content according to the type.

Abstract: One example method involves operations for a processing device that include receiving, by a machine learning model trained to generate a search result, a search query for a text input. The machine learning model is trained by receiving pre-training data that includes multiple documents. Pre-training the machine learning model by generating, using an encoder, feature embeddings for each of the documents included in the pre-training data. The feature embeddings are generated by applying a masking function to visual and textual features in the documents. Training the machine learning model also includes generating, using the feature embeddings, output features for the documents by concatenating the feature embeddings and applying a non-linear mapping to the feature embeddings. Training the machine learning model further includes applying a linear classifier to the output features. Additionally, operations include generating, for display, a search result using the machine learning model based on the input.

Abstract: This disclosure covers methods, non-transitory computer readable media, and systems analyze a digital design document having an initial layout of digital objects and automatically generate candidate layouts by concurrently performing operations on the digital objects within the initial layout. By iteratively performing concurrent operations, in some implementations, the methods, non-transitory computer readable media, and systems produce multiple candidate layouts that the systems evaluate by generating design scores. Based on a comparison of such design scores, the methods, non-transitory computer readable media, and systems generate one or more modified layouts (from among the candidate layouts) for presentation to a user.

Abstract: Systems and methods are provided for capturing by a camera of a user device, a first image depicting a first environment of the user device; overlaying a first virtual object on a portion of the first image depicting the first environment; modifying a surface of the first virtual object using content captured by the user device; storing a second virtual object comprising the first virtual object with the modified surface; and generating for display the second virtual object on a portion of a second image depicting a second environment.

Abstract: An automated communication design analysis and construction system that includes one or more intelligent communication design servers, comprising: a normalization module that converts communication content files for different recipients to normalized intermediate format files; an objects identification and quantification module that identifies text objects and image objects in the normalized intermediate format files; a cross-recipient group analysis module configured to identify static global objects that are invariant between recipients, data variables, and variable global objects that vary between recipients in the normalized intermediate format files; and an intelligent communication content learning and constructing engine that can construct standard communication design files based on the static global objects, the data variables, and the variable global objects. A data storage stores the communication content files and the standard communication design files.

Abstract: This disclosure relates generally to method and system for determining label value for labels in unstructured documents. Typical systems have challenge in understanding variations in layout of unstructured documents and extract information therefrom. The disclosed method and system facilitate systematically identifying sections and bounding boxes in the page images, taking image portion of the bounding boxes and extracting labels and label values therefrom. In case the label values are not present in the same bounding box having the label, the neighboring labels are examined for the matching label values. The system also obtains label-label value pairs from the document by utilizing a trained deep learning model, and compares the output with the label-label value pairs extracted earlier. An aggregated confidence score is assigned to the text in the bounding box.

Abstract: Image encoded documents are identified by recognizing known objects in each document with an object recognizer. The objects in each page are filtered to remove lower order objects. Known features in the objects are recognized by sequentially organizing each object in each filtered page into a one-dimensional array, where each object is positioned in a corresponding one-dimensional array as a function of location in the corresponding filtered page. The one-dimensional array is then compared to known arrays to classify the image document corresponding to the one-dimensional array.

Abstract: Digital content design system techniques are described using baseline units to control arrangement and sizing of digital content. In one example, a digital content design system receives a user input specifying a number of baselines to be included within an available display area of a page. Baselines are used to align digital content to control arrangement of the digital content within the page, e.g., text. From this, the digital content design system then calculates a baseline unit from a distance used to space adjacent baselines of the number of baselines from each other. This baseline unit is then leveraged by the system as a fundamental unit of measure to control arrangement and/or sizing of digital content in relation to each other.

Abstract: Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

Abstract: In implementations of systems for generating snap guides relative to glyphs of editable text rendered in a user interface using a font, a computing device implements a snap guide system to receive input data describing a position of a cursor relative to the glyphs of the editable text in the user interface. The glyphs of the editable text are enclosed within a bounding box having a height that is less than a height of an em-box of the font. The snap guide system generates a first group of snap guides for the glyphs of the editable text which includes a snap guide for each side of the bounding box and a snap guide for an x-height of the font. The snap guide system generates an indication of a particular snap guide of the first group of snap guides for display in the user interface based on the position of the cursor.

Abstract: A system may include a computer readable medium and a processor communicatively coupled to the computer readable medium. The processor may be configured to: obtain a graphical image file, the graphical image file including an image, wherein the image includes at least one sequence of repeating pattern elements, each of the at least one sequence including the repeating pattern elements that are repeated along a linear direction; and convert the graphical image file to at least one file including hardware directives that when executed cause a recreation of the image of the graphical image file to be drawn, wherein a file size of the at least one file is smaller than the graphical image file.

Abstract: A spatial indexing system receives a set of walkthrough videos of an environment taken over a period of time and receives an image search query that includes an image of an object. The spatial indexing system searches the set of walkthrough videos for instances of the object. The spatial indexing system presents search results in a user interface, displaying in a first portion a 2D map associated with one walkthrough video with marked locations of instances of the object and a second portion with a histogram of instances of the object over time in the set of walkthrough videos.

Abstract: Various embodiments disclosed herein are directed to methods of capturing Vehicle Identification Numbers (VIN) from images captured by a mobile device. Capturing VIN data can be useful in several applications, for example, insurance data capture applications. There are at least two types of images supported by this technology: (1) images of documents and (2) images of non-documents.

Abstract: In some embodiments, a method can include executing a first model to extract a first region of interest (ROI) image and a second ROI image from an image that shows an item and an indication of information associated to the item. The first ROI image can include a portion of the image showing the item and the second ROI image can include a portion of the image showing the indication of information. The method can further include executing a second model to identify the item from the first ROI image and generate a representation of the item. The method can further include executing a third model to read the indication of information associated to the item from the second ROI image and generate a representation of information.

Abstract: In a collection technique, a user (such as a taxpayer) provides information (such as income-tax information) by submitting an image of a document, such as an income-tax summary or form. In particular, the user may provide a description of the document. In response, the user is prompted for the information associated with the field in the document. Then, the user provides the image of a region in the document that includes the field. Based on the image, the information is extracted, and the field in the form is populated using the extracted information. The prompting, receiving, extracting and populating operations may be repeated for one or more additional fields in the document.

Abstract: Disclosed are an apparatus and a method for detecting whether an anomalous sentence having a context different from that of other sentences exists in a document. The apparatus for detecting a contextually-anomalous sentence in a document according to the present invention includes: a sentence encoder for encoding individual sentences constituting document data by means of a predetermined rule (function) to generate encoding vectors; a context embedder neural network for converting the generated encoding vector into embedding vectors corresponding thereto; and a context anomaly detector neural network for detecting whether an anomalous sentence exists in the converted document data.

Abstract: An automated method and system for validating (cross-validating) data fields in an electronic document, such as a document that has been passed through an optical character recognition (“OCR”) or Intelligent Document Recognition (“IDR”) system or software, to improve accuracy of the electronic document.

Abstract: An image processing engine and method of forming a hologram of a target image for projection using data streaming. An input or primary image is sub-sampled using a kernel and the secondary image output used to generate a hologram of the target image. A technique of kernel sub-sampling using a plurality of two or more data streams provides improvements in efficiency, including reduced data storage requirements and increased processing speed.

Abstract: Image data having text associated with a plurality of text-field types is received, the image data including target image data and context image data. The target image data including target text associated with a text-field type. The context image data providing a context for the target image data. A trained neural network that is constrained to a set of characters for the text-field type is applied to the image data. The trained neural network identifies the target text of the text-field type using a vector embedding that is based on learned patterns for recognizing the context provided by the context image data. One or more predicted characters are provided for the target text of the text-field type in response to identifying the target text using the trained neural network.

Abstract: Systems and methods for digitized document image data spillage recovery are provided. One or more memories may be coupled to one or more processors, the one or more memories including instructions operable to be executed by the one or more processors. The one or more processors may be configured to capture an image; process the image through at least a first pass to generate a first contour; remove a preprinted bounding region of the first contour to retain text; generate one or more pixel blobs by applying one or more filters to smudge the text; identify the one or more pixel blobs that straddle one or more boundaries of the first contour; resize the first contour to enclose spillage of the one or more pixel blobs; overlay the text from the image within the resized contour; and apply pixel masking to the resized contour.

Abstract: An online system extracts information from non-fixed form documents. The online system receives an image of a form document and obtains a set of phrases and locations of the set of phrases on the form image. For at least one field, the online system determines key scores for the set of phrases. The online system identifies a set of candidate values for the field from the set of identified phrases and identifies a set of neighbors for each candidate value from the set of identified phrases. The online system determines neighbor scores, where a neighbor score for a candidate value and a respective neighbor is determined based on the key score for the neighbor and a spatial relationship of the neighbor to the candidate value. The online system selects a candidate value and a respective neighbor based on the neighbor score as the value and key for the field.

Abstract: An image processing apparatus includes one or more processors; and a memory, the memory storing instructions, which when executed by the one or more processors, cause the one or more processors to generate vertical direction distribution data indicating a frequency distribution of distance values with respect to a vertical direction of a range image, from the range image having distance values according to distance of a road surface in a plurality of captured images captured by a plurality of imaging parts; set a search range corresponding to a predetermined reference point in the vertical direction distribution data and extract a plurality of pixels from the search range; and detect a road surface, based on the plurality of extracted pixels.

Abstract: The present application discloses a method and an apparatus for optical character recognition, an electronic device and a storage medium, and relates to the fields of artificial intelligence and deep learning. The method may include: determining, for a to-be-recognized image, a text bounding box of a text area therein, and extracting a text area image from the to-be-recognized image according to the text bounding box; determining a bounding box of text lines in the text area image, and extracting a text-line image from the text area image according to the bounding box; and performing text sequence recognition on the text-line image, and obtaining a recognition result. The application of the solution in the present application can improve a recognition speed and the like.

Abstract: Data processing systems (e.g. image processing systems) and methods are provided for processing a stream of data values (e.g. pixel values). In each of a plurality of iterations, a respective particular data value of the stream is processed by operating on a respective particular subset of data values of the stream. In each iteration: group indication data for at least one group is retrieved and used to define a set of groups into which data values within the particular subset can be grouped; each of the data values within the particular subset is grouped into one of the groups of the set of groups; the particular data value is processed using one or more of the data values of the particular subset in dependence on the classification of the data values into the groups; and group indication data is stored for a group, for use in a subsequent iteration.

Abstract: In a method for generating synthetic medical image data, first image data of an object under examination including a first value for a property is acquired, second image data of the object under examination including a second value for the property is acquired, the second value of the property of the second image data is matched to the first value to modify the second image data to generate synthetic image data, and the synthetic image data is provided (e.g. in electronic form as a data file). The first image data can be captured with a first magnetic resonance device at a first point in time, and the second image data can be captured with a second magnetic resonance device at a second point in time.

Abstract: An information processing apparatus includes a memory and a processor configured to acquire an image of a digitized document and execute a first verification process by using image processing using artificial intelligence. The first verification process verifies whether a first requirement is satisfied. The first requirement is a specific requirement among multiple requirements that are required when the acquired image of the document is stored. The processor is also configured to execute a second verification process by using a determination process not using the artificial intelligence. The second verification process verifies whether a second requirement among the multiple requirements is satisfied. The second requirement is other than the first requirement.

Abstract: A method is provided for encoding a digital video to provide for improved color mapping.

Abstract: Systems, methods, and products for auto tagging structured PDF documents that do not have accessibility tags. In one embodiment, structured PDF documents having accessibility tags are first parsed and analyzed to organize the visual components of the documents. The relationships of the identified objects to DOM elements (e.g., tags) are determined, and the objects and related DOM elements are stored in training files. The training files are used to train various classifiers. Untagged PDF documents are then parsed to identify included visual objects, and the classifiers are used to determine DOM elements that should be associated with visual objects identified in the untagged PDF documents. This information is used to construct a DOM structure corresponding to each untagged document. A new PDF is then generated corresponding to each untagged document using the generated DOM structure and visual object information.

Abstract: An information processing apparatus includes: a network interface to communicate with a server for managing content data generated during an event, the content data including at least text data converted from audio data collected during the event and screenshot data of a screen captured during the event; and circuitry to control a display to display one or more items of text data, and one or more images of screenshot data, side by side, in a temporal order.

Abstract: A system can merge text bounding boxes such as Optical Character Recognition (OCR) bounding boxes. A document can comprise a plurality of the text bounding boxes. Distance thresholds between text bounding boxes can be utilized for comparison against a distance threshold. Distance thresholds can vary depending on context information associated with the document. In response to a determination that text bounding boxes satisfy the distance threshold, the text bounding boxes can be assigned to a bounding box group.

Abstract: An information processing apparatus includes a processor. The processor is programmed to: control a display to display a plurality of recognition results, each recognition result being a recognition result of a document, the document having a plurality of items and an entry field for each item, each recognition result being displayed for each corresponding item of the document; acquire a checking order for each item, the checking order being an order in which each of the displayed recognition results has been checked by a user viewing the displayed recognition results; and change a display order by using the acquired checking order, the display order being an order in which to display a subsequent set of recognition result.

Abstract: An image processing system accesses an image of a completed form document. The image of the form document includes one or more features, such as form text, at particular locations within the image. The image processing system accesses a template of the form document and computes a rotation and zoom of the image of the form document relative to the template of the form document based on the locations of the features within the image of the form document relative to the locations of the corresponding features within the template of the form document. The image processing system performs a rotation operation and a zoom operation on the image of the form document, and extracts data entered into fields of the modified image of the form document. The extracted data can be then accessed or stored for subsequent use.

Abstract: A computer-implemented method for extracting information from a document, for example an official document, is disclosed. The method comprises acquiring an input image comprising a document portion; performing image segmentation on the input image to form a binary input image that distinguishes the document portion from the remaining portion of the input image; estimating a first image transform to align the binary input image to a binary template image, using the first image transform on the input image to form an intermediate image; estimating a second image transform to align the intermediate image to a template image; using the second image transform on the intermediate image to form an output image; and extracting a field from the output image using a predetermined field of the template image.

Abstract: An image of a document is received from an image capture device, the image being in a format of an image file. At least one location of a user input field is automatically detected within the image based on patterns previously detected in a set of other images that were annotated to identify locations of user input fields within the individual images of the set. Coordinates are determined for the at least one location, and an electronic document is generated based on the received image. Generation of the electronic document includes addition of a software user input component at the location within the image with use of the coordinates, the software user input component configured to receive input from a user in electronic form.

Abstract: This disclosure involves the automatic sizing and placement of text within an image background. For example, a computing system obtains reference font size information for a font type to be applied to message text for display on a digital image. The computing system detects, within an image background of the digital image, a target region having proportions that enclose the message text based on the reference font size information. The computing system determines a target font size for the message text. The target font size allows the message text, when rendered in the font type at the target font size, to fit within the target region of the image background. The computing system generates a combined digital image by rendering the message text in the font type at the target font size within the target region of the image background.

Abstract: A system for managing documents, comprising: interfaces to a user interface, proving an application programming interface, a database of document images, a remote server, configured to communicate a text representation of the document from the optical character recognition engine to the report server, and to receive from the remote server a classification of the document; and logic configured to receive commands from the user interface, and to apply the classifications received from the remote server to the document images through the interface to the database. A corresponding method is also provided.

Abstract: Variation in received documents types and templates used for each document type poses challenge in developing a generic background noise removal approach for automatic text information extraction technique. Embodiments herein provide a method and a system for document classification and text information extraction. Time efficient and accurate text detection engine-based Region of Interest (ROI) technique is provided to accurately identify text region followed by a multi-layered neural network based architecture for enhanced classification accuracy to identify the type of document. A multistage image pre-processing approach is provided for efficient, effective, and accurate background noise removal from the classified document, which includes unsupervised clustering, identification, segmentation, masking, contour approximation, selective subtraction, and dynamic thresholding.

Abstract: Results of character recognition processing for a scanned image of a document and a setting item set to a property attached to the scanned image of a document are obtained. Displaying on a screen having a preview area where the scanned image of a document is displayed and an editing area where information input in the setting item is edited, that is, displaying the scanned image of a document in the preview area and displaying the setting item and the information in the editing area are controlled. A selection for the setting item displayed in the editing area is detected. A verification rule set to the detected setting item is obtained. A character recognition area satisfying the verification rule is extracted from the results of the character recognition processing. A character recognition area displayed on the preview area and extracted is highlighted.

Abstract: In some embodiments, a method includes training a first machine learning model based on multiple documents and multiple templates associated with the multiple documents. The method further includes executing the first machine learning model to generate multiple relevancy masks, the multiple relevancy masks to remove a visual structure of the multiple templates from a visual structure of the multiple documents. The method further includes generating multiple multichannel field images to include the multiple relevancy masks and at least one of the multiple documents or the multiple templates. The method further includes training a second machine learning model based on the multiple multichannel field images and multiple non-native texts associated with the multiple documents. The method further includes executing the second machine learning model to generate multiple non-native texts from the multiple multichannel field images.

Abstract: Techniques for document segmentation. In an example, a document processing application segments an electronic document image into strips. A first strip overlaps a second strip. The application generates a first mask indicating one or more elements and element types in the first strip by applying a predictive model network to image content in the first strip and a prior mask generated from image content of the first strip. The application generates a second mask indicating one or more elements and element types in the second strip by applying the predictive model network to image content in the second strip and the first mask. The application computes, from a combined mask derived from the first mask and the second mask, an output electronic document that identifies elements in the electronic document and the respective element types.

Abstract: An image processing device includes an image reader that reads an image, a first determiner that determines whether character crushing occurs when the image is binarized, a second determiner that determines a rate of a photographic region in the image, and a controller that performs conversion into monochrome N-gradation image data based on determination results of the first and second determiners.

Abstract: Disclosed are a method and device for determining an icon position. The method includes: detecting a target object in a target image and determining the reference position of the target object in the target image, and detecting a salient position in the target image, thereby obtaining the reference position of a key target or object in the target image, and a salient position possibly requiring more attention in the target image; and selecting, according to the distance between the reference position or salient position and preset candidate positions, an icon position from the candidate positions.

Abstract: Computer vision systems and methods for text classification are provided. The system detects a plurality of text regions in an image and generates a bounding box for each detected text region. The system utilizes a neural network to recognize text present within each bounding box and classifies the recognized text, based on at least one extracted feature of each bounding box and the recognized text present within each bounding box, according to a plurality of predefined tags. The system can associate a key with a value and return a key-value pair for each predefined tag.

Abstract: A system and method are capable of ensuring that one or more text strings will be able to be fully rendered in a target area of a user interface or a target area of a graphics file. The system and method determine the number of pixels of first and second reference text that fit in the target area in the horizontal direction and the vertical direction, respectively, determine the number of pixels of string text in the horizontal direction and the vertical direction, and compare the number of pixels in the horizontal direction of the first reference text and the vertical direction of the second reference text respectively to the number of pixels in the horizontal direction and the vertical direction of the text string that is desired to be rendered in the target area to determine whether the text string will fit in the target area.

Abstract: An information processing system includes circuitry configured to accept a selection of specification information from a list of the specification information displayed on a display, the specification information being included in form information acquired by performing form recognition; and display, on the display, an input field in which journal information based on the selected specification information is input.

Abstract: Root cause estimation for a data set corresponding to customer returns of a product may use a probabilistic model to associate customer-entered product return data with probability distributions relating to possible root causes for the returns. A particular application relates to applying a Bayesian network to customer-selected return reason codes and customer-entered return reason comments to estimate a probability distribution for root causes of a plurality of returns and uncertainties relating to the probability distribution estimation. A bag-of-n-grams can be used to enable the Bayesian network to process natural language portions of the customer-entered product return data. The output of the model and other data relating to the root cause estimation can be conveyed to a seller of the returned products via a user interface.