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Document image binarization using background estimation and stroke edges

International journal on document analysis and recognition, 2010-12, Vol.13 (4), p.303-314 [Peer Reviewed Journal]

Springer-Verlag 2010 ;2015 INIST-CNRS ;ISSN: 1433-2833 ;EISSN: 1433-2825 ;DOI: 10.1007/s10032-010-0130-8

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  • Title:
    Document image binarization using background estimation and stroke edges
  • Author: Lu, Shijian ; Su, Bolan ; Tan, Chew Lim
  • Subjects: Applied sciences ; Artificial intelligence ; Computer Science ; Computer science; control theory; systems ; Exact sciences and technology ; Image Processing and Computer Vision ; Original Paper ; Pattern Recognition ; Pattern recognition. Digital image processing. Computational geometry
  • Is Part Of: International journal on document analysis and recognition, 2010-12, Vol.13 (4), p.303-314
  • Description: Document images often suffer from different types of degradation that renders the document image binarization a challenging task. This paper presents a document image binarization technique that segments the text from badly degraded document images accurately. The proposed technique is based on the observations that the text documents usually have a document background of the uniform color and texture and the document text within it has a different intensity level compared with the surrounding document background. Given a document image, the proposed technique first estimates a document background surface through an iterative polynomial smoothing procedure. Different types of document degradation are then compensated by using the estimated document background surface. The text stroke edge is further detected from the compensated document image by using L1-norm image gradient. Finally, the document text is segmented by a local threshold that is estimated based on the detected text stroke edges. The proposed technique was submitted to the recent document image binarization contest (DIBCO) held under the framework of ICDAR 2009 and has achieved the top performance among 43 algorithms that are submitted from 35 international research groups.
  • Publisher: Berlin/Heidelberg: Springer-Verlag
  • Language: English
  • Identifier: ISSN: 1433-2833
    EISSN: 1433-2825
    DOI: 10.1007/s10032-010-0130-8
  • Source: Alma/SFX Local Collection
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