A Novel Block Segmentation And Classification Algorithm In Mixed Text/Graphic/Image/Table Documents

Bing Shan Chien; Bor Shenn Jeng; Sheng Hua Lu; Yu Ping Lan; Ming Wen Chang

doi:10.1117/12.962363

30 January 1990 A Novel Block Segmentation And Classification Algorithm In Mixed Text/Graphic/Image/Table Documents

Bing Shan Chien, Bor Shenn Jeng, Sheng Hua Lu, Yu Ping Lan, Ming Wen Chang

Proceedings Volume 1153, Applications of Digital Image Processing XII; (1990) https://doi.org/10.1117/12.962363
Event: 33rd Annual Technical Symposium, 1989, San Diego, United States

Abstract

The block segmentation and block classification of digitized printed documents segmented into region of texts, graphics, tables and images are very important in document analysis and understanding. Conventionally, the Constrained Run Length Algorithm (CRLA) has been proposed to segment digited document, but failure may occur due to improper constraints. Especially, it usually leads to failure about block segmentation when the documents are complicated and inclined. They could only deal with the text part for block classification without any certain rules, and couldn't succeed in effective classification and even lead to wrong classification. In this paper, a powerful approach for document analysis named "Automatic Local Sequential Segmentation and Hierarchical classification" is proposed. Our results show that this algorithm is an efficient approach for block segmentation and block classification.

Citation Download Citation

Bing Shan Chien, Bor Shenn Jeng, Sheng Hua Lu, Yu Ping Lan, and Ming Wen Chang "A Novel Block Segmentation And Classification Algorithm In Mixed Text/Graphic/Image/Table Documents", Proc. SPIE 1153, Applications of Digital Image Processing XII, (30 January 1990); https://doi.org/10.1117/12.962363

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