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Published in:
2013 | OriginalPaper | Chapter
Language Adaptive Methodology for Handwritten Text Line Segmentation
Authors : Subhash Panwar, Neeta Nain, Subhra Saxena, P. C. Gupta
Published in: Computer Analysis of Images and Patterns
Publisher: Springer Berlin Heidelberg
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Text line segmentation in handwritten documents is a very challenging task because in handwritten documents curved text lines appear frequently. In this paper, we have implemented a general line segmentation approach for handwritten documents with various languages. A novel connectivity strength parameter is used for deciding the groups of the components which belongs to the same line. oversegmentation is also removed with the help of depth first search approach and iterative use of the
CSF
. We have implemented and tested this approach with English, Hindi and Urdu text images taken from benchmark database and find that it is a language adaptive approach which provide encouraged results. The average accuracy of the proposed technique is 97.30%.