Abbyy Development Inc.

Abbyy Software Limited

Nortel Networks Corporation

Sergey Fedorov

Vladimir Rybkin

Andrey Anatolievich Isaev

Mikhail Kostyukov

Ivan Khintsitskiy

Michael Leong

Yuriy Zakharov

Galina Titova

Disclosed are systems, methods and computer program products for using prior frame data for OCR processing of frames in video sources to detect natural language text therein. An example includes receiving a frame from a video source and retrieving prior frame data associated with the video source. The OCR-processing includes using prior frame data to detect blobs similar to blobs described in the prior frame data; using detected similar blobs to detect in the frame character candidates similar to character candidates described in the prior frame data; using detected similar character candidates to detect in the frame text candidates similar to text candidates described in the prior frame data; and using detected similar text candidates to detect in the frame text strings similar to text strings described in the prior frame data.

System and method for using prior frame data for OCR processing of frames in video sources

Detecting blur and defocusing in images is described. After detection, correction algorithms are applied. Detection provides an image processing system with parameters related to a blur (e.g., direction, strength) and noise levels, or may trigger a message to a user to re-take a photograph. Detection involves finding and analyzing edges of objects instead of an entire image. Disclosed detector may be used for OCR purposes, blur and defocusing detection in photographic and scanning devices, video cameras, print quality control systems, computer vision. Detection of blur and defocusing of an image involve second derivatives of image brightness. Object edges are detected. For points on edges, profiles of second derivative are obtained in the direction of the gradient. Statistics are gathered about parameters of profiles in various directions. By analyzing statistics, image distortions and their type (e.g., blur, defocusing), the strength of distortion, the direction of the blur are detected.

Detecting and correcting blur and defocusing

Embodiments of the present invention disclose a method, device and system for restoring a motion-blurred image. The method comprises determining parameters for a one-dimensional Optical Transfer Function (OTF) for the motion-blurred image in Fourier space; determining a signal-to-noise ratio for the motion-blurred image in the Fourier space; and correcting for motion blur based on the parameters of the OTF. Determining the parameters comprises calculating a function &#x3a6;(p,q) which is based on the square of the modulus of the Fourier transform |G(p,q)|of the motion-blurred image. The parameters include the absolute value of the one-dimensional OTF, and the phase and sign of the OTF.

Device and system for restoring a motion-blurred image

Embodiments of the present invention disclose a method and system for restoring a motion-blurred image. The method comprises determining parameters for a one-dimensional Optical Transfer Function (OTF) for the motion-blurred image in Fourier space; determining a signal-to-noise ratio for the motion-blurred image in the Fourier space; and correcting for motion blur based on the parameters of the OTF. Determining the parameters comprises calculating a function &#x3a6;(p,q) which is based on the square of the modulus of the Fourier transform |G(p,q)|of the motion-blurred image. The parameters include the absolute value of the one-dimensional OTF, and the phase and sign of the OTF.

Method and system for restoring a motion-blurred image

A general purpose network tone detection method and apparatus that allows the precise and accurate recognition of North American tones (MF, DTMF (Dual-Tone Multifrequency), and CPT (Call Progress Tones)) and international MF-R2 tones as well as taking into consideration other common tones such as Calling Card Service Prompt and Recall Dial. Through the use of the Discrete Fourier Transform (DFT) on small time windows and by providing phase continuity between these windows, the results of the successive DFTs may be combined and processed by a second DFT computation. This second DFT allows higher frequency resolution without requiring the re-computation of the DFT from the time samples. The resulting effect is a tone receiver with both high time and frequency resolution which consequently leads to robust and accurate tone recognition systems conforming even to the most stringent specification while maintaining low computational requirements.

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Moscow, Russia

Sergey Fedorov