Word-level optimization of dynamic programming-based handwritten word recognition algorithms

Paul D. Gader; Wen-Tsong Chen

doi:10.1117/12.335821

7 January 1999 Word-level optimization of dynamic programming-based handwritten word recognition algorithms

Paul D. Gader, Wen-Tsong Chen

Proceedings Volume 3651, Document Recognition and Retrieval VI; (1999) https://doi.org/10.1117/12.335821
Event: Electronic Imaging '99, 1999, San Jose, CA, United States

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.

Citation Download Citation

Paul D. Gader and Wen-Tsong Chen "Word-level optimization of dynamic programming-based handwritten word recognition algorithms", Proc. SPIE 3651, Document Recognition and Retrieval VI, (7 January 1999); https://doi.org/10.1117/12.335821

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