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Proceedings Paper

A nonparametric classifier for unsegmented text
Author(s): George Nagy; Ashutosh Joshi; Mukkai Krishnamoorthy; Yu Lin; Daniel P. Lopresti; Shashank Mehta; Sharad Seth
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Paper 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.

Paper Details

Date Published: 15 December 2003
PDF: 7 pages
Proc. SPIE 5296, Document Recognition and Retrieval XI, (15 December 2003); doi: 10.1117/12.529291
Show Author Affiliations
George Nagy, Rensselaer Polytechnic Institute (United States)
Ashutosh Joshi, Rensselaer Polytechnic Institute (United States)
Mukkai Krishnamoorthy, Rensselaer Polytechnic Institute (United States)
Yu Lin, Univ. of Nebraska/Lincoln (United States)
Daniel P. Lopresti, Lehigh Univ. (United States)
Shashank Mehta, Indian Institute of Technology Kanpur (India)
Sharad Seth, Univ. of Nebraska/Lincoln (United States)


Published in SPIE Proceedings Vol. 5296:
Document Recognition and Retrieval XI
Elisa H. Barney Smith; Jianying Hu; James Allan, Editor(s)

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