A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of newspaper image blocks using texture analysis
Computer Vision, Graphics, and Image Processing
Text segmentation using Gabor filters for automatic document processing
Machine Vision and Applications - Special issue: document image analysis techniques
Page segmentation and classification
CVGIP: Graphical Models and Image Processing
Handbook of Pattern Recognition and Computer Vision
Handbook of Pattern Recognition and Computer Vision
Recognizing Characters in Scene Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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. Finally feature based connected component merging was introduced to gather homogeneous textual regions together within the scope of its bounding rectangles. The proposed method can be used to locate text in-group of newspaper images with multiple page layouts. Initial results are encouraging, then they are experimented with considerable number of newspaper images with different layout structures and promising results were obtained. This finds its major application in digital libraries for OCR where information can be of different quality depending on the age of the scanned paper.