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Proceedings Paper

Improved document image segmentation algorithm using multiresolution morphology
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Paper Abstract

Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,1 which is also available in his open-source Leptonica library.2The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.

Paper Details

Date Published: 24 January 2011
PDF: 8 pages
Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740D (24 January 2011); doi: 10.1117/12.873461
Show Author Affiliations
Syed Saqib Bukhari, Technical Univ. of Kaiserslautern (Germany)
Faisal Shafait, German Research Ctr. for Artificial Intelligence (Germany)
Thomas M. Breuel, Technical Univ. of Kaiserslautern (Germany)


Published in SPIE Proceedings Vol. 7874:
Document Recognition and Retrieval XVIII
Gady Agam; Christian Viard-Gaudin, Editor(s)

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