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Proceedings Paper

Handwritten and printed text distinction by using stroke thickness features
Author(s): Hong Ding; Huiqun Wu; Jun Wang; Xiaofeng Zhang
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Paper Abstract

This paper presents an algorithm to identify the handwritten and the printed texts among document images. The characteristic of stroke thickness is used and a kind of calculating method is designed for this feature. The proposed method, which is clearly defined and easily realized, calculates the stroke thickness feature by counting edge pixels in a neighborhood. Document images are generally divided into text lines or characters. However, the line and the character are not conducive to the judgment between handwritten and printed text distinction. The line is too rough and the character is too small. Using the stroke thickness characteristics, combined with layout analysis, the text line in the document image is further divided into the area of uniform thickness. This kind of area is more detailed than text line and larger than a single character. So more stable features can be extracted from it. Last, the features of these regions are divided by using SVM. The proposed algorithm obtained better performance in the document image database including handwritten and printed texts.

Paper Details

Date Published: 23 January 2017
PDF: 7 pages
Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103221B (23 January 2017); doi: 10.1117/12.2265363
Show Author Affiliations
Hong Ding, Nantong Univ. (China)
Huiqun Wu, Nantong Univ. (China)
Jun Wang, Jiangsu College of Engineering and Technology (China)
Xiaofeng Zhang, Nantong Univ. (China)


Published in SPIE Proceedings Vol. 10322:
Seventh International Conference on Electronics and Information Engineering
Xiyuan Chen, Editor(s)

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