Abstract
Considering the vast collection of handwritten documents in various archives, research studies for their automatic processing have major impact in the society. Line segmentation from images of such documents is a crucial step. The problem is more difficult for documents of major Indian scripts such as Bangla because a large number of its characters have either ascender or descender or both and the majority of its writers are accustomed in extremely cursive handwriting. In this article, we describe a novel strip based text line segmentation method for handwritten documents of Bangla. Moreover, the proposed method has been found to perform efficiently on English and Devanagari handwritten documents. We conducted extensive experimentations and its results show the robustness of the proposed approach on multiple scripts.
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References
Mullick, K., Banerjee, S., and Bhattecharya, U.: An Efficient Line Segmentation Approach for Handwritten Bangla Document Image. Eighth International Conference on Advences in pattern Recognition (ICAPR), 1–6 (2015)
Alaei, A., Pal, U., and Nagabhushan, P.: A New Scheme for Unconstrained Handwritten Text-Line Segmentation. Pattern Recognition. 44(4), 917–928, (2011)
Papavassiliou, V., Stafylakis, T., Katsouros, V., Carayannis, G.: Handwritten document image segmentation into text lines and words. Pattern Recognition. 147, 369–377 (2010)
Shi, Z., Seltur, S., and Govindaraju, V.: A Steerable Directional Local Profile Technique for Extraction of Handwritten Arabic Text Lines. Proceedings of 10th International Conference on Document Analysis and Recognition, 176–180, (2009)
Louloudis, G., Gatos, B., and Halatsis, C: Text Line and Word Segmentation of Handwritten Documents. Pattern Recognition, 42(12):3169–3183, (2009)
Stamatopoulos, N., Gatos, B., Louloudis, G, Pal, U., Alaei, A.: ICDAR 2013 Handwritten Segmentation Contest. 12th International Conference on Document Analysis and Recognition, 14021–1406 (2013)
Likforman-Sulem, L., Zahour, A., and Taconet, B.: Text Line Segmentation of Historical Documents: a Survey. International Journal of Document Analysis and Recognition: 123–138, (2007)
Antonacopoulos, A., Karatzas, D.: Document Image analysis for World War II personal records, International Workshop on Document Image Analysis for Libraries. DIAL, 336–341 (2004)
Li, y., Zheng, Y., Doermann, D., and Jaeger, S.: A new algorithm for detecting text line in handwritten documents. International Workshop on Frontiers in Handwriting Recognition, 35–40 (2006)
Louloudis, G. Gatos, B., Pratikakis, I., Halatsis, K., Alaei, A.: A Block Based Hough Transform Mapping for Text Line Detection in Handwritten Documents. Proceedings of the Tenth International Workshop on Frontiers in Handwriting Recognition, 515–520 (2006)
Tsuruoka, S., Adachi, Y., and Yoshikawa, T.: Segmentation of a Text-Line for a Handwritten Unconstrained Document Using Thinning Algorithm, Proceedings of the 7th International Workshop on Frontiers in Handwriting Recognition:505–510, (2000)
Luthy, F., Varga, T., and Bunke, H.,: Using Hidden Markov Models as a Tool for Handwritten Text Line Segmentation. Ninth International Conference on Document Analysis and Recognition. 9, 630–632 (2007)
Lie, Y., Zheng, Y.: Script-Independent Text Line Segmentation in Freestyle Handwritten Documents. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(8), 1313–1329 (2008)
Yin, F., Liu, C: A Variational Bayes Method for Handwritten Text Line Segmentation. International Conference on Document Analysis and Recognition. 10, 436–440 (2009)
Brodic, D., and Milivojevic, Z.: Text Line Segmentation by Adapted Water Flow Algorithm. Symposium on Neural Network Applications in Electrical Engineering. 10, 225–229 (2010)
Dinh, T. N., Park, J., Lee, G.: Voting Based Text Line Segmentation in Handwritten Document Images. International Conference on Computer and Information Technology. 10, 529–535 (2010)
Biswas, B., Bhattacharya, U., and Chaudhuri, B.B.: A Global-to-Local Approach to Binarization of Degraded Document Images. 22nd International Conference on Pattern Recognition, 3008–3013 (2014)
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Biswas, B., Bhattacharya, U., Chaudhuri, B.B. (2017). A Robust Scheme for Extraction of Text Lines from Handwritten Documents. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_10
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DOI: https://doi.org/10.1007/978-981-10-2107-7_10
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