Abstract
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%.
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Panwar, S., Nain, N., Saxena, S., Gupta, P.C. (2013). Language Adaptive Methodology for Handwritten Text Line Segmentation. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_41
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DOI: https://doi.org/10.1007/978-3-642-40261-6_41
Publisher Name: Springer, Berlin, Heidelberg
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