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Proceedings Paper

A new pre-classification method based on associative matching method
Author(s): Yutaka Katsuyama; Akihiro Minagawa; Yoshinobu Hotta; Shinichiro Omachi; Nei Kato
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Paper Abstract

Reducing the time complexity of character matching is critical to the development of efficient Japanese Optical Character Recognition (OCR) systems. To shorten processing time, recognition is usually split into separate preclassification and recognition stages. For high overall recognition performance, the pre-classification stage must both have very high classification accuracy and return only a small number of putative character categories for further processing. Furthermore, for any practical system, the speed of the pre-classification stage is also critical. The associative matching (AM) method has often been used for fast pre-classification, because its use of a hash table and reliance solely on logical bit operations to select categories makes it highly efficient. However, redundant certain level of redundancy exists in the hash table because it is constructed using only the minimum and maximum values of the data on each axis and therefore does not take account of the distribution of the data. We propose a modified associative matching method that satisfies the performance criteria described above but in a fraction of the time by modifying the hash table to reflect the underlying distribution of training characters. Furthermore, we show that our approach outperforms pre-classification by clustering, ANN and conventional AM in terms of classification accuracy, discriminative power and speed. Compared to conventional associative matching, the proposed approach results in a 47% reduction in total processing time across an evaluation test set comprising 116,528 Japanese character images.

Paper Details

Date Published: 18 January 2010
PDF: 8 pages
Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340K (18 January 2010); doi: 10.1117/12.838842
Show Author Affiliations
Yutaka Katsuyama, Fujitsu Labs. Ltd. (Japan)
Akihiro Minagawa, Fujitsu Labs. Ltd. (Japan)
Yoshinobu Hotta, Fujitsu Labs. Ltd. (Japan)
Shinichiro Omachi, Tohoku Univ. (Japan)
Nei Kato, Tohoku Univ. (Japan)

Published in SPIE Proceedings Vol. 7534:
Document Recognition and Retrieval XVII
Laurence Likforman-Sulem; Gady Agam, Editor(s)

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