Abstract
In this paper, we proposed a new approach for Hindi character recognition using digital curvelet transform. Curvelet transform well approximate the curved singularities of images therefore very useful for feature extraction to character images. A Devanagari script contains more than 49 characters (13 vowels and 33 consonants) and all the characters are rich in curve information. The input image is segmented first then curvelet features are obtained by calculating statistics of thick and thin images by applying curvelet transform. The system is trained with K-Nearest Neighbor classifier. The experiments are evaluated with in-house dataset containing 200 images of character set (each image contains all Hindi characters). The results obtained are very promising with more than 90% recognition accuracy.
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© 2011 Springer-Verlag Berlin Heidelberg
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Verma, G.K., Prasad, S., Kumar, P. (2011). Handwritten Hindi Character Recognition Using Curvelet Transform. In: Singh, C., Singh Lehal, G., Sengupta, J., Sharma, D.V., Goyal, V. (eds) Information Systems for Indian Languages. ICISIL 2011. Communications in Computer and Information Science, vol 139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19403-0_37
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DOI: https://doi.org/10.1007/978-3-642-19403-0_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19402-3
Online ISBN: 978-3-642-19403-0
eBook Packages: Computer ScienceComputer Science (R0)