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Proceedings Paper

Word-level optimization of dynamic programming-based handwritten word recognition algorithms
Author(s): Paul D. Gader; Wen-Tsong Chen
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Paper Abstract

In the standard segmentation-based approach to lexicon- driven handwritten word recognition, character recognition algorithms are generally trained on isolated characters and individual character-class confidence scores are combined to estimate confidences in the various hypothesized identities for a word. In this paper, results from investigating alternatives to these standard methods are presented. We refer to these alternative methods as system-level optimization methods.

Paper Details

Date Published: 7 January 1999
PDF: 8 pages
Proc. SPIE 3651, Document Recognition and Retrieval VI, (7 January 1999); doi: 10.1117/12.335821
Show Author Affiliations
Paul D. Gader, Univ. of Missouri/Columbia (United States)
Wen-Tsong Chen, Univ. of Missouri/Columbia (United States)

Published in SPIE Proceedings Vol. 3651:
Document Recognition and Retrieval VI
Daniel P. Lopresti; Jiangying Zhou, Editor(s)

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