ConTEXTract™ Video OCR | SRI International

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ConTEXTract™ Video OCR

ConTEXTract™ video OCR technology finds and reads text (such as street signs, name tags, and billboards) in real scenes. Recognition of text within imagery and video requires a more specialized approach than is provided by off-the-shelf OCR software, which is designed primarily for recognizing text within documents. The software is available for license from SRI.

ConTEXTract distinguishes lines of text from other contents in the imagery, processes the lines, and then sends them to an OCR submodule, which recognizes the text. Any OCR engine can be integrated into ConTEXTract with minor modifications.

ConTEXTract is a configurable, real-time, end-to-end solution that provides a scalable architectural framework adaptable to a wide variety of imagery and processing environments. Unique capabilities developed by SRI for ConTEXTract include:

  • The ability to distinguish text from non-text in video imagery
  • The ability to extract text from complex, patterned, and/or colored backgrounds
  • Character recognition despite oblique viewing angles, low resolution, and other distortions
  • High accuracy by combining text information that appears over multiple video frames

Development of ConTEXTract is an outgrowth of several activities. For the U.S. Postal Service, SRI ran an R&D program for more than 10 years to read addresses on mail pieces, on which text is often located amid graphics and printed on complex patterned backgrounds. SRI also conducted government-sponsored research to extract information (including text captions and scene text) from large volumes of video imagery, including broadcast news.

SRI's ConTEXTract software was licensed to Autonomy Virage, Inc., a leading vendor of content-based video indexing and information extraction systems, for incorporation into VideoLogger, a video cataloging product, as a plug-in module.

SRI was funded by ARDA (a predecessor to IARPA) under the Video Analysis and Content Extraction (VACE) program to extend our video text recognition techniques to include reading scene text written on any planar surface, even if viewed at an oblique angle.

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