A nonparametric classifier for unsegmented text

George Nagy; Ashutosh Joshi; Mukkai Krishnamoorthy; Yu Lin; Daniel P. Lopresti; Shashank Mehta; Sharad Seth

doi:10.1117/12.529291

15 December 2003 A nonparametric classifier for unsegmented text

George Nagy, Ashutosh Joshi, Mukkai Krishnamoorthy, Yu Lin, Daniel P. Lopresti, Shashank Mehta, Sharad Seth

Proceedings Volume 5296, Document Recognition and Retrieval XI; (2003) https://doi.org/10.1117/12.529291
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Symbolic Indirect Correlation (SIC) is a new classification method for unsegmented patterns. SIC requires two levels of comparisons. First, the feature sequences from an unknown query signal and a known multi-pattern reference signal are matched. Then, the order of the matched features is compared with the order of matches between every lexicon symbol-string and the reference string in the lexical domain. The query is classified according to the best matching lexicon string in the second comparison. Accuracy increases as classified feature-and-symbol strings are added to the reference string.

Citation Download Citation

George Nagy, Ashutosh Joshi, Mukkai Krishnamoorthy, Yu Lin, Daniel P. Lopresti, Shashank Mehta, and Sharad Seth "A nonparametric classifier for unsegmented text", Proc. SPIE 5296, Document Recognition and Retrieval XI, (15 December 2003); https://doi.org/10.1117/12.529291

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