Kim Joong Soo, Lee Jeong Hwan, Choi Heung Moon

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 2, No. 6, pp. 937-946, Nov. 1995
10.3745/KIPSTE.1995.2.6.937,   PDF Download:

Abstract

We proposed an efficient block segmentation and classification method for the document analysis using SGLDM(spatial gray level dependence matrix) and BP(back propagation) neural network. Seven texture features are extracted directly from the SGLDM of each gray-level block image, and by using the nonlinear classifier of neural network BP, we can classify document blocks into 9 categories. The proposed method classifies the equation block, the table block and the flow chart block, which are mostly composed of the characters, out of the blocks that are conventionally classified as non-character blocks. By applying Sobel operator on the gray-level document image before binarization, we can reduce the effect of the background noises and by using the additional horizontal-vertical smoothing aw well as the vertical-horizontal smoothing of images, we can obtain an effective block segmentation that does not lead to the segmentation into small pieces. The result of experiment shows that a document can be segmented and classified into the character blocks of large fonts, medium fonts, small fonts, the character recognigible candidates of tables, flow charts, equations, and the non-character blocks of photos, figures, and graphs.

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