caa a22 4500 469051531 CHVBK 20180323132832.0 cr unu---uuuuu 170328e19920601xx s 000 0 eng 10.1007/BF02626996 doi (NATIONALLICENCE)springer-10.1007/BF02626996 Text segmentation using gabor filters for automatic document processing [Elektronische Daten] [Anil Jain, Sushil Bhattacharjee] There is a considerable interest in designing automatic systems that will scan a given paper document and store it on electronic media for easier storage, manipulation, and access. Most documents contain graphics and images in addition to text. Thus, the document image has to be segmented to identify the text regions, so that OCR techniques may be applied only to those regions. In this paper, we present a simple method for document image segmentation in which text regions in a given document image are automatically identified. The proposed segmentation method for document images is based on a multichannel filtering approach to texture segmentation. The text in the document is considered as a textured region. Nontext contents in the document, such as blank spaces, graphics, and pictures, are considered as regions with different textures. Thus, the problem of segmenting document images into text and nontext regions can be posed as a texture segmentation problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. These filters have been extensively used earlier for a variety of texture segmentation tasks. Here we apply the same filters to the document image segmentation problem. Our segmentation method does not assume any a priori knowledge about the content or font styles of the document, and is shown to work even for skewed images and handwritten text. Results of the proposed segmentation method are presented for several test images which demonstrate the robustness of this technique. Springer-Verlag New York Inc., 1992 Jain Anil Pattern Recognition and Image Processing Processing Laboratory, Michigan State University, 48824-1027, E. Lansing, MI, USA aut Bhattacharjee Sushil Pattern Recognition and Image Processing Processing Laboratory, Michigan State University, 48824-1027, E. Lansing, MI, USA aut Machine Vision and Applications Springer-Verlag 5/3(1992-06-01), 169-184 0932-8092 5:3<169 1992 5 138 https://doi.org/10.1007/BF02626996 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF02626996 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Jain Anil Pattern Recognition and Image Processing Processing Laboratory, Michigan State University, 48824-1027, E. Lansing, MI, USA aut NATIONALLICENCE 700 1- Bhattacharjee Sushil Pattern Recognition and Image Processing Processing Laboratory, Michigan State University, 48824-1027, E. Lansing, MI, USA aut NATIONALLICENCE 773 0- Machine Vision and Applications Springer-Verlag 5/3(1992-06-01), 169-184 0932-8092 5:3<169 1992 5 138 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer