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Proceedings Paper

AdaBoost-based handwritten/printed discrimination on a single character
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Paper Abstract

Handwritten and machine-printed characters are recognized separately in most OCR systems due to their distinct difference. In applications where both kinds of characters are involved, it is necessary to judge a character’s handwritten/printed property before feeding it into the proper recognition engine. In this paper, a new method to discriminate between handwritten and machine-printed character is proposed. Unlike most previous works, the discrimination we carried out in this paper is totally based on single character. Five kinds of statistical features are extracted from character image, then feature selection and classification are implemented simultaneously by a learning algorithm based on AdaBoost. Experiments on large data sets have demonstrated the effectiveness of the method.

Paper Details

Date Published: 13 January 2003
PDF: 8 pages
Proc. SPIE 5010, Document Recognition and Retrieval X, (13 January 2003); doi: 10.1117/12.476027
Show Author Affiliations
Hailong Liu, Tsinghua Univ. (China)
Xiaoqing Ding, Tsinghua Univ. (China)
Chi Fang, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 5010:
Document Recognition and Retrieval X
Tapas Kanungo; Elisa H. Barney Smith; Jianying Hu; Paul B. Kantor, Editor(s)

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