Digital Image Enhancement of Indic Historical Manuscripts

  • Zhixin ShiEmail author
  • Srirangaraj Setlur
  • Venu Govindaraju
Part of the Advances in Pattern Recognition book series (ACVPR)


Historical documents in Indic scripts can be found on a wide range of media such as paper, palm leaves, and parchment. Palm leaves are believed to be one of the earliest forms of writing media and their use as writing material has been recorded in various parts of the world including India. Ancient palm leaf manuscripts relating to religion, science, medicine, astronomy are still available for reference today due to many ongoing efforts for preservation of ancient documents by libraries and universities around the world. These manuscripts typically last a few centuries but with time the leaves degrade and the writing becomes illegible. Image processing techniques can help enhance the images of these manuscripts so as to enable readability of the written text. In this chapter, we propose methods for enhancing digital images of palm leaf and other historical manuscripts. We approximate the background of a gray-scale image using piece-wise linear and nonlinear models. Normalization algorithms are used on the color channels of the palm leaf image to obtain an enhanced gray-scale image. Experimental results show significant improvement in readability. An adaptive local connectivity map is used to try to segment lines of text from the enhanced images with the objective of facilitating techniques such as keyword spotting or partial OCR and thereby making it possible to index these documents for retrieval from a digital library.


Image enhancement Image processing Document pre-processing Historical documents Palm leaf manuscripts Indic scripts OCR Text line extraction 


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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Zhixin Shi
    • 1
    Email author
  • Srirangaraj Setlur
    • 2
  • Venu Govindaraju
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity at BuffaloBuffaloUSA
  2. 2.Department of Computer Science and Engineering Center for Unified Biometrics and SensorsUniversity at BuffaloAmherstUSA

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