Abbyy Production LLC

Abbyy Development Inc.

Aleksey Ivanovich Kalyuzhny

Aleksey Yevgenyevich Lebedev

Aleksey Yevgen'yevich Lebedev

Systems and methods for performing OCR of an image depicting text symbols and imaging a document having a plurality of planar regions are disclosed. An example method comprises: receiving a first image of a document having a plurality of planar regions and one or more second images of the document; identifying a plurality of coordinate transformations corresponding to each of the planar regions of the first image of the document; identifying, using the plurality of coordinate transformations, a cluster of symbol sequences of the text in the first image and in the one or more second images; and producing a resulting OCR text comprising a median symbol sequence for the cluster of symbol sequences.

Optical character recognition of documents having non-coplanar regions

Systems and methods for performing OCR of a series of images depicting text symbols. An example method comprises: receiving, by a processing device, a current image of a series of images of an original document, wherein the current image at least partially overlaps with a previous image of the series of images; performing optical character recognition (OCR) of the current image to produce an OCR text and a corresponding text layout; associating at least part of the OCR text with a first cluster of a plurality of clusters of symbol sequences associated with one or more previously received images of the series of images; identifying a first string representing the first cluster of symbol sequences based on a first subset of images of the series of images; identifying a first template field of a document template corresponding to the first cluster based on the first string representing the first cluster and the text layout of the current image; identifying, for the first cluster, a second-level median based on one or more parameters of the first template; and producing, using the second-level string, a resulting OCR text representing at least a portion of the first template field of the original document.

Method and system for optical character recognition of series of images

Systems and methods for performing OCR of a series of images depicting text symbols. An example method comprises performing OCR a series of images to produce a current symbol sequence and corresponding symbol sequence quadrangle; associating the current symbol sequence with a previous symbol sequence for a previously received image; identifying a median string; determining a median symbol sequence quadrangle; and displaying, using the median symbol sequence quadrangle, a resulting OCR text representing at least a portion of the original document.

Optical character recognition of series of images

The subject matter of this specification can be implemented in, among other things, a method that includes receiving a first image from a first camera depicting a first view of a physical item, where the physical item displays a plurality of characters. The method includes receiving a second image from a second camera depicting a second view of the physical item. The method includes performing optical character recognition on the first image to identify first characters and a first layout in the first image and on the second image to identify second characters and a second layout in the second image. The method includes combining the first characters with the second characters by comparing the first characters with the second characters and the first layout with the second layout. The method includes storing the combined first and second characters.

Using multiple cameras to perform optical character recognition

The present disclosures provide methods of optical character recognition for a patterned document having one static element and one information field. Systems and methods are disclosed to identify in each of a current and a previous image of a series of images of an original document overlapping with each other, a corresponding plurality of base points, wherein each base point is associated with one textural artifact in each of the current image and the previous image using an OCR text of the current image; identify parameters of a coordinate transformation converting coordinates of the previous image into coordinates of the current image; associate a part of the OCR text with a cluster of a plurality of clusters of symbol sequences; identify a median string representing the cluster of symbol sequences; and produce a resulting OCR text representing at least a portion of the original document.

Data entry from series of images of a patterned document

Systems and methods for performing OCR of a series of images depicting text symbols. An example method comprises: receiving, by a processing device, a current image of a series of images of an original document, wherein the current image at least partially overlaps with a previous image of the series of images; performing optical symbol recognition (OCR) of the current image to produce an OCR text and a corresponding text layout; associating, using a coordinate transformation, at least part of the OCR text with a first cluster of a plurality of clusters of symbol sequences, wherein the OCR text is produced by processing the current image and wherein the symbol sequences are produced by processing one or more previously received images of the series of images; identifying a first median string representing the first cluster of symbol sequences based on a first subset of images of the series of images; identifying a first template field of a document template corresponding to the first cluster based on the first median string representing the first cluster and the text layout of the current image; analyzing the symbol sequences from the first cluster to identify suitable symbol sequences, wherein the suitable symbol sequences satisfy first parameters of the first template field; identifying, for the first cluster, a second-level median string representing the cluster of symbol sequences based on a plurality of the suitable symbol sequences; producing, using the second-level median string, a resulting OCR text representing at least a portion of the first template field of the original document.

Systems and methods for performing OCR of an image depicting text symbols. An example method comprises: receiving an original image of a document; identifying location of a symbol sequence in the image; performing a series of conversion operations on a the portion of the image containing the identified symbol sequence; performing OCR of the symbol sequence in the resulting images; and combining these OCR results to produce a resulting OCR text representing at least a portion of the original document.

Method for improving quality of recognition of a single frame

Systems and methods for performing OCR of a series of images depicting text symbols. An example method comprises performing OCR a series of images to produce symbol sequences and corresponding symbol sequence quadrangles; identifying median string; calculating transformation of the symbol sequence quadrangles into a common coordinate system; determining distances between the transformed symbol sequence quadrangles; identifying a median symbol sequence quadrangle; displaying, using the median symbol sequence quadrangle, a resulting OCR text representing at least a portion of the original document.

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.

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.

Verification of optical character recognition results

Systems and methods for performing optical character recognition (OCR) are disclosed. An example method may include receiving a current image that overlaps with a previous image of a series of images of an original document; performing OCR of the current image to produce an OCR text; identifying a plurality of textual artifacts in the images that are each represented by a sequence of symbols having a frequency of occurrence within the OCR text falling below a threshold frequency; identifying corresponding base points that are each associated with a textural artifact; identifying parameters of a coordinate transformation converting coordinates of the previous image into coordinates of the current image; associating part of the OCR text with a cluster of symbol sequences, the symbol sequences being produced by processing previously received images; identifying a median string representing the cluster; and producing a resulting OCR text representing a portion of the original document.

Systems and methods are described for receiving a current image that partially overlaps with a previous image of a series of images of an original document; performing optical character recognition (OCR) of the current image, producing an OCR text and a corresponding text layout; identifying textual artifacts in the current and previous images, each represented by a sequence of symbols having a frequency of occurrence within the OCR text below a threshold frequency; identifying corresponding base points associated with textual artifacts; identifying parameters of a coordinate transformation converting coordinates of the previous image into coordinates of the current image; associating part of the OCR text with a cluster of symbol sequences, wherein the symbol sequences are produced by processing previously received images; identifying an order of clusters of symbol sequences reflecting a layout of the original document; and producing a resulting OCR text representing a portion of the original document.

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Moscow, Russia

Aleksey Ivanovich Kalyuzhny