Abstract: In some embodiments, provided are procedures for processing images that may have different font sizes. In some embodiments, it involves OCR'ing with multiple passes at different resolutions.

Abstract: Disclosed embodiments of the invention provide automated global optimization methods and systems of OCR, tailored to each document being digitized. A document-specific database is created from an OCR scan of a document of interest, which contains an exhaustive listing of words in the document. Images of each word, taken from all the fonts encountered, are entered into the database and mapped to a corresponding textual representation. After entry of a first instance of an image of a word written in a particular font, each new occurrence of the word in that font can be quickly recognized by image processing techniques. The disclosed methods and systems may be used in conjunction with adaptive character recognition training and word recognition training of the OCR engines.

Abstract: An electronic model of the image document is created by undergoing an OCR process. The electronic model includes elements (e.g., words, text lines, paragraphs, images) of the image document that have been determined by each of a plurality of sequentially executed stages in the OCR process. The electronic model serves as input information which is supplied to each of the stages by a previous stage that processed the image document. A graphical user interface is presented to the user so that the user can provide user input data correcting a mischaracterized item appearing in the document. Based on the user input data, the processing stage which produced the initial error that gave rise to the mischaracterized item corrects the initial error. Stages of the OCR process subsequent to this stage then correct any consequential errors arising in their respective stages as a result of the initial error.

Abstract: The present invention provides a method for an Optical Character Recognition (OCR) system providing recognition of characters that are partly hidden by crossing outs due to for example an imprint of a stamp, handwritten signatures, etc. The method establishes a set of template images of certainly recognized characters from the image of the text being processed by the OCR system, wherein the effect of the crossed out section is modelled into the template images before comparing these images with the image of a visually impaired crossed out character. The modelled template image having the highest similarity with the visually impaired crossed out character is the correct identification for the visually impaired character instance.

Abstract: The present disclosure is directed toward systems and methods for assisting users in correcting OCR errors. For example, systems and methods described herein involve identifying the position of a cursor within a machine-readable document. Systems and methods described herein also involve identifying corresponding position co-ordinates in a source image, as well as, capturing an image preview from the source image based on the corresponding position co-ordinates. Systems and methods described herein may also involve providing the preview of the source image within the machine-readable document.

Abstract: Methods and systems for localizing numbers and characters in captured images. A side image of a vehicle captured by one or more cameras can be preprocessed to determine a region of interest. A confidence value of series of windows within regions of interest of different sizes and aspect ratios containing a structure of interest can be calculated. Highest confidence candidate regions can then be identified with respect to the regions of interest and at least one region adjacent to the highest confidence candidate regions. An OCR operation can then be performed in the adjacent region. An identifier can then be returned from the adjacent region in order to localize numbers and characters in the side image of the vehicle.

Abstract: An image processing apparatus is provided that includes a character chopper component that segments words into individual characters in a bitmap of a textual image undergoing an OCR process. The Character chopper component is configured to produce a set of (possibly curved) chop-lines which divide a bitmap of any given word into its individual character or glyph candidates. Cases where an input bitmap contains two separate words are handled by marking a place where those words should be split. The character segmentation algorithm computes the set of vertically oriented, curved chop-lines by considering glyph and background colors in a given word bitmap. The set is filtered afterwards using various heuristics, in order to preserve those lines that indeed do separate a word's glyphs and minimize the number of those that do not.

Abstract: Systems, methods and computer program products on storage devices for shape clustering and applications in processing various documents, including an output of an optical character recognition (OCR) process. The output of an OCR process is classified into a plurality of clusters of clip images and a representative image for each cluster is generated to identify clusters whose clip images were incorrectly assigned character codes by the OCR process.

Abstract: A method of processing an image includes receiving a digital version of the image, processing the digital version of the image through at least two binarization processes to thereby create a first binarization and a second binarization, and processing the first binarization through a first optical character recognition process to thereby create a first OCR output file. Processing the first binarization through a first optical character recognition process includes compiling first metrics associated with the first OCR output file. The method also includes processing the second binarization through the first optical character recognition process to thereby create a second OCR output file. Processing the second binarization through the first optical character recognition process includes compiling second metrics associated with the second OCR output file. The method also includes using the metrics, at least in part, to select a final OCR output file from among the OCR output files.

Abstract: The positioning of elements of a broken word can be corrected by receiving an optical character recognition (OCR) conversion of a printed publication and identifying multiple parts of the broken word from the OCR conversion to place in a graphical user interface (GUI). The multiple parts can be placed in the GUI using original positioning data for the printed publication. A user can make a selection in the GUI indicating that multiple parts from the OCR conversion are of the broken word and can automatically adjust bounds of the multiple parts to form a corrected word.

Abstract: The positioning of elements of a broken word can be corrected by receiving an optical character recognition (OCR) conversion of a printed publication and identifying multiple parts of the broken word from the OCR conversion to output in a graphical user interface (GUI). The multiple parts can be placed in the GUI using original positioning data for the printed publication. A user can make a selection in the GUI indicating that multiple parts from the OCR conversion are of the broken word and can automatically adjust bounds of the multiple parts to form a corrected word.

Abstract: An approach is provided to generate forms with template inclusions. In the approach, optical character recognition (OCR) text is compared to corresponding text in a selected form. Characters of text in the OCR text are then replaced with text from the template text, the replacing results in a form with template inclusions. The form with template inclusions is then processed by a forms processing operation.

Abstract: Techniques for shape clustering and applications in processing various documents, including an output of an optical character recognition (OCR) process.

Abstract: Line segmentation in an OCR process is performed to detect the positions of words within an input textual line image by extracting features from the input to locate breaks and then classifying the breaks into one of two break classes which include inter-word breaks and inter-character breaks. An output including the bounding boxes of the detected words and a probability that a given break belongs to the identified class can then be provided to downstream OCR or other components for post-processing. Advantageously, by reducing line segmentation to the extraction of features, including the position of each break and the number of break features, and break classification, the task of line segmentation is made less complex but with no loss of generality.

Abstract: Line segmentation in an OCR process is performed to detect the positions of words within an input textual line image by extracting features from the input to locate breaks and then classifying the breaks into one of two break classes which include inter-word breaks and inter-character breaks. An output including the bounding boxes of the detected words and a probability that a given break belongs to the identified class can then be provided to downstream OCR or other components for post-processing. Advantageously, by reducing line segmentation to the extraction of features, including the position of each break and the number of break features, and break classification, the task of line segmentation is made less complex but with no loss of generality.

Abstract: The present invention relates to an optical character recognition device (OCR) for reading a form provided with character frames in reading fields, into which a user fills each character. Characteristic vectors are extracted from the character images of each frame. A number of characters decision unit 16, into which the characteristic vectors are input, decides the number of characters filled in one of the character frames. A character separation unit 18 separates each of characters from the character image based on the number of characters decided by the decision unit 16. The character recognition unit 20 then recognizes each of the character. The OCR according o the present invention is able to read the form correctly, in which a plurality of characters are filled in one of the frames.

Abstract: A method of post-processing character data from an optical character recognition (OCR) engine and apparatus to perform the method. This exemplary method includes segmenting the character data into a set of initial words. The set of initial words is word level processed to determine at least one candidate word corresponding to each initial word. The set of initial words is segmented into a set of sentences. Each sentence in the set of sentences includes a plurality of initial words and candidate words corresponding to the initial words. A sentence is selected from the set of sentences. The selected sentence is word disambiguity processed to determine a plurality of final words. A final word is selected from the at least one candidate word corresponding to a matching initial word. The plurality of final words is then assembled as post-processed OCR data.

Abstract: An imaging-based bar code reader that includes an imaging and decoding system. Focusing optics and a sensor array define a field of view. A data processor has a memory for storing a pattern definition of previously imaged OCR characters and comparing a format of said previously stored characters to a present image to determine a character content of the present image.

Abstract: A method of processing uniform mailpieces referred to as a “run” of mailpieces, during which method OCR is performed for recognizing certain information in a zone of interest of an image of each mailpiece, and during which method the following steps are performed: a) initializing a matrix accumulator associated with said run and including unitary accumulation elements that correspond to the pixels of the image; b) consolidating said matrix accumulator by incrementing certain unitary accumulation elements by deriving an indication of the spatial position of a block of pixels in which said certain information has been recognized unambiguously, or by using construction and local graphical correlation of blocks of image pixels to derive an optical flow map indicating local graphical movements; and c) defining, in the OCR processing, said zone of interest on the basis of the unitary accumulation elements of the consolidated matrix accumulator that present extreme accumulation values.

Abstract: In one embodiment, there is provided a method for an Optical Character Recognition (OCR) system. The method comprises: recognizing an input character based on a plurality of classifiers, wherein each classifier generates an output by comparing the input character with a plurality of trained patterns; grouping the plurality of classifiers based on a classifier grouping criterion; and combining the output of each of the plurality of classifiers based on the grouping.