AdaBoost-based handwritten/printed discrimination on a single character

Hailong Liu; Xiaoqing Ding; Chi Fang

doi:10.1117/12.476027

13 January 2003 AdaBoost-based handwritten/printed discrimination on single character

Hailong Liu, Xiaoqing Ding, Chi Fang

Proceedings Volume 5010, Document Recognition and Retrieval X; (2003) https://doi.org/10.1117/12.476027
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

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.

Citation Download Citation

Hailong Liu, Xiaoqing Ding, and Chi Fang "AdaBoost-based handwritten/printed discrimination on single character", Proc. SPIE 5010, Document Recognition and Retrieval X, (13 January 2003); https://doi.org/10.1117/12.476027

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