Abstract: A document marking projection system receives a target document comprising text content, determines a set of similar documents using an index of stored documents, where the set of similar documents are similar to the target document, and selects a first similar document from the set of similar documents that is most similar to the target document. The document marking projection system determines one or more portions of text content in the first similar document that are different from respective one or more portions of text content in the target document, determines a first location of a first marking within the first similar document, determines a projected marking for the target document in view of one or more differences between the first portion of the text content in the first similar document and a respective portion of the text content in the target document, and stores the projected marking for the target document.

Abstract: A distortion correction component of a mobile device receives an image of a spread open multi-page document, determines a binding edge line of the spread open multi-page document, determines a first set of substantially vertical straight lines lying left of the binding edge line and a second set of substantially vertical straight lines lying right of the binding edge line. The distortion correction component then determines a first vanishing point based on the first set of substantially vertical straight lines and a second vanishing point based on the second set of substantially vertical straight lines. A first quadrangle is determined based on the first vanishing point and a second quadrangle is determined based on the second vanishing point. A corrected image for the first page is generated based on the first quadrangle and a corrected image for the second page is generated based on the second quadrangle.

Abstract: A page orientation component of an image processing device receives an image of a document, transforms the image to a binarized image by performing a binarization operation on the image, and identifies a portion of the binarized image that comprises one or more rows of textual content. The page orientation component identifies a plurality of horizontal runs of white pixels and a plurality of vertical runs of white pixels in the one or more rows of textual content in the portion of the binarized image. The page orientation component generates a first histogram for the plurality of horizontal runs of white pixels, and a second histogram for the plurality of vertical runs of white pixels, and determines an orientation of the one or more rows of textual content in the image based on the first histogram and the second histogram.

Abstract: The present disclosures provide methods of optical character recognition for extracting information from a patterned document, which have at least static element and at least one information field. Related computer systems and computer-readable non-transitory storage media are also disclosed.

Abstract: Systems and methods for redacting certain content (e.g., content representing private, privileged, confidential, or otherwise sensitive information) from electronic documents. An example method comprises: identifying, by a computing device, two or more layers in an electronic document; processing each of the identified layers to produce a layer text representing one or more objects comprised by the layer; combining the produced layer texts to produce a combined text of the electronic document; and identifying, within the combined text of the electronic document, a target character string corresponding, in view of a specified search function, to a specified character string.

Abstract: A method of verifying optical character recognition (OCR) results may involve: performing OCR on one or more initial images of a document and displaying initial OCR results of the document to a user; receiving a feedback from the user regarding an error location in the initial OCR results, the error location being a location of a misspelled character sequence; receiving an additional image of the document, which corresponds to the error location, and performing OCR of the additional image to produce additional OCR results; identifying a cluster of character sequences, which correspond to the error location, using the initial OCR results and the additional OCR results; identifying an order of character sequences in the cluster of character sequences based on their respective probability values; and displaying to the user modified optical character recognition results, which contain in the error location a corrected character sequence.

Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed create and store a decision tree, the nodes of which include classifiers that each recognizes the symbol that corresponds to a symbol image. Input of a symbol image to the decision tree and processing of the symbol image through one or more nodes of the decision tree returns a symbol corresponding to the symbol image.

Abstract: Systems and methods for identifying screenshots within document images. An example method comprises: receiving an image of at least a part of a document; identifying, within the image, a polygonal object having a visually distinct border comprising a plurality of edges of one or more intersecting rectangles; asserting a screenshot image hypothesis with respect to the identified polygonal object; and responsive to evaluating at least one condition associated with one or more attributes of the identified polygonal object, classifying the identified polygonal object as a screenshot image.

Abstract: Disclosed are systems, computer-readable mediums, and methods for creating a flexible structure description. To create the flexible structure description an image of a document of a particular document type that contains a table is received. An entry describing an item in the table is received. Title elements within the document are searched for based upon the entry. Data fields and anchor elements are detected for the entry. A flexible structure description for the particular document type is generated that includes a set of search elements for each data field in the image of the document and the title elements. The flexible structure description is matched against the image. Data from the image is extracted based upon the matching of the flexible structure description against the image.

Abstract: Systems and method for improving the quality of document images are provided. One method includes identifying a plurality of image fragments within a previously received document image that includes text. The method further includes separating the plurality of image fragments into a plurality of classes. Each class includes a subset of the plurality of image fragments that are substantially similar to one another. The method further includes, for each of the plurality of classes: (1) processing a class of image fragments to generate a combined and substantially enlarged image fragment for the class; and (2) filtering the combined and substantially enlarged image fragment to generate a filtered image fragment for the class. The method further includes generating an improved document image by replacing or modifying the image fragments within the document image based on the filtered image fragments.

Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed carry out an initial processing step on one or more scanned images to identify, for each symbol image within a scanned document, a set of graphemes that match, with high frequency, symbol patterns that, in turn, match the symbol image. The set of graphemes identified for a symbol image is associated with the symbol image as a set of candidate graphemes for the symbol image. The set of candidate graphemes are then used, in one or more subsequent steps, to associate each symbol image with a most likely corresponding symbol code.

Abstract: Systems and methods for identifying transformations to be applied to at least part of a document image for improving the OCR quality. An example method comprises: constructing, by a computer system, an ordered list of transformations to be applied to an image comprising a character string, each transformation corresponding to a hypothesis asserted with respect to one or more characteristics of the image; applying, to the image, a leading transformation on the list to produce a transformed image; evaluating a quality of the transformed image to produce a quality estimate; and updating the list in view of the quality estimate.

Abstract: Methods and systems for performing character recognition of a document image include analyzing verification performed by a user on a recognized text obtained by character recognition of a document image, identifying analogous changes of a first incorrect character for a first correct character, and prompting the user to initiate a training of a recognition pattern based on the identified analogous changes.

Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed carry out an initial processing step on one or more scanned images to identify a set of graphemes that most likely correspond to each symbol image that occurs in the scanned document image. The graphemes are selected for a symbol image based on accumulated votes generated from symbol patterns identified as likely related to the symbol image using one or more decision forests.

Abstract: Aspects of the present disclosure relate to image segmentation for data verification. A method of the disclosure comprises: receiving, using a processing device, an image of at least a part of a document; identifying a first image region in the image that corresponds to data to be verified by a user; extracting data from the image of at least the part of the document partitioning the image into a plurality of image segments based on positioning information related to the first image region, wherein the plurality of image segments comprises a first image segment and a second image segment, and wherein the second image segment comprises the first image region; and presenting data extracted from the first image region in association with the first image segment and the second image segment.

Abstract: Methods are described for efficient and substantially instant recognition and translation of text in photographs. A user is able to select an area of interest for subsequent processing. Optical character recognition (OCR) may be performed on the wider area than that selected for determining the subject domain of the text. Translation to one or more target languages is performed. Manual corrections may be made at various stages of processing. Variations of translation are presented and made available for substitution of a word or expression in the target language. Translated text is made available for further uses or for immediate access.

Abstract: In accordance with a first aspect of the invention, there is provided a method comprising receiving an input as part of a translation request from a requestor, performing a first translation of the input; wherein the first translation is a machine translation, returning the first translation to the requestor; and based on feedback on the first translation from the requestor performing the following (a) fragmenting the input into multiple translation jobs, (b) distributing the multiple translation jobs to a plurality of human translators; (c) generating a second translation of the input based on translations of the multiple jobs by the human translators; and (d) returning the second translation to the requestor.

Abstract: Methods for correcting distortions in an image including text, or an image of a page that includes text, are disclosed. The methods include identifying reliable and substantially straight lines from elements in the image. Vanishing points are determined from the lines. Parameters associated with a rectangle are determined. A coordinate conversion is performed.

Abstract: Disclosed are systems, computer-readable mediums, and methods for detecting glare in a frame of image data. A frame of image data is preprocessed. A set of connected components in the preprocessed frame is determined. A set of statistics is calculated for one or more connected components in the set of connected components. A decision for the one or more connected components is made, using the calculated set of statistics, if the connected component is a light spot over text. Whether glare is present in the frame is determined.