Heuristics for test recognition using contextual information

Tony Baraghimian

doi:10.1117/12.200792

31 January 1995 Heuristics for test recognition using contextual information

Tony Baraghimian

Proceedings Volume 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities; (1995) https://doi.org/10.1117/12.200792
Event: 23 Annual AIPR Workshop: Image and Information Systems: Applications and Opportunities, 1994, Washington, DC, United States

Abstract

Competitive electronic imaging systems are emerging due to rapidly declining processing power and storage costs. Imaging converts information on paper to electronic pictures. For applications involving large quantities of paper documents, the resulting pictures are further processed by automated character recognition systems, resulting in a text representation of the original document. Current character recognition accuracy varies from one implementation to the next, and greatly depends on each particular application. We define a set of information fusion rules for combining character recognition system output. The combined result has a higher character recognition accuracy and lower error rate than either of the individual recognizer outputs taken separately. This new set of fusion heuristics takes advantage of the following information from multiple text string recognition systems simultaneously: (1) multiple hypotheses and associated confidences for each character in a text string; (2) multiple text string segmentation hypotheses; (3) separate or combined hypotheses for both uppercase and lowercase alphabetic characters; and (4) overall text string hypotheses and associated confidences. Traditionally, only the last of these four information groups is used for fusion of multiple classifications within character recognition systems. We report on a nationally sponsored character recognition benchmark, with results indicating increased accuracy using the heuristic rules described.

Citation Download Citation

Tony Baraghimian "Heuristics for test recognition using contextual information", Proc. SPIE 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities, (31 January 1995); https://doi.org/10.1117/12.200792

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KEYWORDS

Optical character recognition

Associative arrays

Image segmentation

Image processing

Digital image processing

Systems modeling

Computing systems

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