Novel block segmentation and processing for Chinese-English document

Bing-Shan Chien; Bor-Shenn Jeng; San-Wei Sun; Gan-How Chang; Keh-Hwa Shyu; Chun-Hsi Shih

doi:10.1117/12.50377

1 November 1991 Novel block segmentation and processing for Chinese-English document

Bing-Shan Chien, Bor-Shenn Jeng, San-Wei Sun, Gan-How Chang, Keh-Hwa Shyu, Chun-Hsi Shih

Proceedings Volume 1606, Visual Communications and Image Processing '91: Image Processing; (1991) https://doi.org/10.1117/12.50377
Event: Visual Communications, '91, 1991, Boston, MA, United States

Abstract

The block segmentation and block classification of digitized printed documents segmented into regions of texts, graphics, tables, and images are very important in automatic document analysis and understanding. Conventionally, the constrained run length algorithm (CRLA) has been proposed to segment digitized documents, however, it is space-consuming and time- consuming. The CRLA method must define some constrained parameters, so it cannot proceed automatically, and its performance may degrade significantly due to improper parameters. This paper proposes an efficient and effective method for document analysis, sequence connected segmentation and mapping matrix cell algorithm (SCSMMC). This method can analyze both simple and complex documents automatically and it need not define any constraint parameters. This method, which only needs one-reading image of document, can proceed completely and the techniques of segmentation, classification, labeling, and character segmentation proceed at the same time. The proposed document analysis method may also combine with the optical character recognizer to form an adaptive document understanding system.

Citation Download Citation

Bing-Shan Chien, Bor-Shenn Jeng, San-Wei Sun, Gan-How Chang, Keh-Hwa Shyu, and Chun-Hsi Shih "Novel block segmentation and processing for Chinese-English document", Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); https://doi.org/10.1117/12.50377

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