Text extraction from gray scale document images using edge information

In this paper we present a well designed method that makes use of edge information to extract textual blocks from gray scale document images. It aims at detecting textual regions on heavy noise infected newspaper images and separate them from graphical regions. The algorithm traces the feature points in different entities and then groups those edge points of textual regions. From using the technology of line approximation and layout categorization, it can successfully retrieve directional placed text blocks. Finally feature based connected component merging was introduced to gather homogeneous textual regions together within the scope of its bounding rectangles. We can obtain correct page decomposition with efficient computation and reduced memory size by handling line segments instead of small pixels. The proposed method has been tested on a large group of newspaper images with multiple page layouts, promising results approved the effectiveness of our method.