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Proceedings Paper

Complexity reduction with recognition rate maintained for online handwritten Japanese text recognition
Author(s): Jinfeng Gao; Bilan Zhu; Masaki Nakagawa
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Paper Abstract

The paper presents complexity reduction of an on-line handwritten Japanese text recognition system by selecting an optimal off-line recognizer in combination with an on-line recognizer, geometric context evaluation and linguistic context evaluation. The result is that a surprisingly small off-line recognizer, which alone is weak, produces nearly the best recognition rate in combination with other evaluation factors in remarkably small space and time complexity. Generally speaking, lower dimensions with less principle components produce a smaller set of prototypes, which reduce memory-cost and time-cost. It degrades the recognition rate, however, so that we need to compromise them. In an evaluation function with the above-mentioned multiple factors combined, the configuration of only 50 dimensions with as little as 5 principle components for the off-line recognizer keeps almost the best accuracy 97.87% (the best accuracy 97.92%) for text recognition while it suppresses the total memory-cost from 99.4 MB down to 32 MB and the average time-cost of character recognition for text recognition from 0.1621 ms to 0.1191 ms compared with the traditional offline recognizer with 160 dimensions and 50 principle components.

Paper Details

Date Published: 24 January 2012
PDF: 8 pages
Proc. SPIE 8297, Document Recognition and Retrieval XIX, 82970A (24 January 2012); doi: 10.1117/12.911682
Show Author Affiliations
Jinfeng Gao, Tokyo Univ. of Agriculture and Technology (Japan)
Bilan Zhu, Tokyo Univ. of Agriculture and Technology (Japan)
Masaki Nakagawa, Tokyo Univ. of Agriculture and Technology (Japan)


Published in SPIE Proceedings Vol. 8297:
Document Recognition and Retrieval XIX
Christian Viard-Gaudin; Richard Zanibbi, Editor(s)

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