Abstract
Segmentation of individual characters from a scanned word image is the most critical step of a typical optical character recognition (OCR) system. A robust segmentation algorithm is proposed in this paper. The word images are segmented into individual characters after skew angle correction and the thinning process, to get the single pixel stroke width. Ligatures of the touching characters are detected by keeping in view the geometrical shape of the English alphabets. The proposed vertical segmentation technique is used to cut individual characters from the handwritten cursive words. The proposed algorithm delivers excellent segmentation accuracy when tested on a local database.
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References
Tan, J., et al.: A new handwritten character segmentation method based on nonlinear clustering. Neurocomputing 89, 213–219 (2012)
Fujisawa, H.: Forty years of research in character and document recognition-an industrial perspective. Pattern Recogn. 41, 2435–2446 (2008)
Saeed, K., Albakoor, M.: Region growing based segmentation algorithm for typewritten and handwritten text recognition. Appl. Soft Comput. 9, 608–617 (2009)
Choudhary, A., Rishi, R., Ahlawat, A.: A New character segmentation approach for off-line cursive handwritten words. Proc. Comput. Sci. 17, 88–95 (2013)
Marti, U., Bunke, H.: The IAM database: an english sentence database for off-line handwriting recognition. Int. J. Doc. Anal. Recogn. 15, 65–90 (2002)
Hull, J.J.: A database for handwritten text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 16, 550–554 (1994)
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Choudhary, A., Kumar, V. (2018). A Robust Technique for Handwritten Words Segmentation into Individual Characters. In: Agrawal, S., Devi, A., Wason, R., Bansal, P. (eds) Speech and Language Processing for Human-Machine Communications. Advances in Intelligent Systems and Computing, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-6626-9_11
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DOI: https://doi.org/10.1007/978-981-10-6626-9_11
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