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Script-Independent Text Segmentation from Document Images

Author

Listed:
  • Parul Sahare

    (Indian Institute of Information Technology, Nagpur, India)

  • Jitendra V. Tembhurne

    (Indian Institute of Information Technology, Nagpur, India)

  • Mayur R. Parate

    (Indian Institute of Information Technology, Nagpur, India)

  • Tausif Diwan

    (Indian Institute of Information Technology, Nagpur, India)

  • Sanjay B. Dhok

    (Visvesvaraya National Institute of Technology, Nagpur, India)

Abstract

Document image analysis finds broad application in the digital world for the purpose of information retrieval. This includes optical character recognition (OCR), indexing of digital libraries, web image processing, etc. One of the important steps in this field is text segmentation. This segmentation becomes complicated for the documents containing text of uneven spacing and characters of varying font sizes. In this paper, script-independent text-line segmentation and word segmentation algorithms are presented. Fast marching method is used for text-line segmentation, whereas wavelet transform with connected components (CCs) labeling is used for word segmentation. Fast marching method is used as a region growing process that detects potential text-lines. For word segmentation, energy map is calculated using wavelet transform to create text-blocks. Both the proposed algorithms are evaluated on different databases containing documents of different scripts, where highest text-line and word segmentation accuracies of 98.9% and 99.1%, respectively, are obtained.

Suggested Citation

  • Parul Sahare & Jitendra V. Tembhurne & Mayur R. Parate & Tausif Diwan & Sanjay B. Dhok, 2022. "Script-Independent Text Segmentation from Document Images," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 13(1), pages 1-21, January.
    • Handle: RePEc:igg:jaci00:v:13:y:2022:i:1:p:1-21
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