Abbyy Software Limited

Abbyy Development Inc.

Vadim Tereshchenko

Konstantin Anisimovich

Vladimir Rybkin

Dmitry Vnuchkov

Alexander Shamis

Sergey Platonov

A method and system for recognizing all varieties of objects in an image by using structure models are disclosed. Structural elements are sought when comparing a structural model with an image but only within a framework of one or more generated hypotheses. The method for identifying objects includes preliminarily creating a structural model of objects by specifying a plurality of basic geometric structural elements corresponding to one or more portions of the object, recording a spatial characteristic of each identified basic geometric structural element, and recording a relational characteristic for each specified basic geometric structural element. Objects in the image are isolated and a list of hypotheses for each object is provided. Hypotheses are tested by determining if the corresponding group of basic geometric structural elements corresponds to another supposed object described in a classifier. Results of testing of hypotheses may be saved and the results may be used to identify objects.

Method of using structural models for optical recognition

The present invention discloses a multilevel method of bitmapped image analysis that comprises a whole image data representation via its components&#x2014;objects of different levels of complexity&#x2014;hierarchically connected therebetween by spatially-parametrical links. The said method comprises preliminarily generating a classifier of the objects that possibly may be present in the image consisting of one or more levels differing in complexity; parsing the image into objects; attaching each object to one of predetermined levels; establishing hierarchical links between objects of different levels; establishing links between objects within the same level; and performing an object feature analysis. The objects feature analysis comprises at least generating and examining a hypothesis about object features and correcting the object's features of the same and other levels in response to the hypothesis examination results. The step of object features analysis may also comprise execution of a recursive RX-cut within the same level.

Multilevel bit-mapped image analysis method

The present invention discloses a method of character and text recognition of a bit-mapped graphic file received from an optical scanning device. The method comprises a trainable template cache, a preliminarily trained feature analysis means, and a context analysis means. The present invention discloses the way to use said means for achieving the best results in recognition. The method supposes that the template cache along with the context analysis means are used as the main shape characteristic analyzing means. The feature analysis means along with the context analysis means are used as subsidiary shape characteristic analyzing means and as a training means for the template cache. The method comprises applying the main shape characteristic analyzing means and optionally applying the subsidiary shape characteristic analyzing means if no or not enough reliability of recognition is achieved after the template cache analyzing. The obtained results are analyzed and sent to the template cache for template training.

Text recognition method using a trainable classifier

A method is described of bit-mapped image analysis comprising division of all analysis means at one's disposal into several groups differing in accuracy and further processing multi-stage analysis. The analysis comprises a primary analysis stage and at least one profound analysis stage, with supplemental data collected at both stages. The primary analysis, includes preliminary recognition of objects with distortion and detection of objects that require more precise analysis means to overcome the distortion. At the primary analysis stage, the analysis means from the group of the most inaccurate group are used. The profound analysis stage includes repeating recognition of objects with a distortion taking into account the supplemental data obtained at the previous stage, detecting objects that require more precise analysis means to overcome the distortion, and collection of newly appeared supplemental data. Each subsequent profound analysis stage uses analysis means from the group of more accurate means.

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Moscow, Russia

Vadim Tereshchenko