Abstract
Offline Character Recognition is one of the most challenging areas in the domain of pattern recognition and computer vision. Here we propose a novel method for Offline Malayalam Character Recognition using multiple classifier combination technique. From the preprocessed character images, we have extracted two features: Chain Code Histogram and Fourier Descriptors. These features are fed as input to two feedforward neural networks. Finally, the results of both neural networks are combined using a weighted majority technique. The proposed system is tested using two schemes- Writer independant and Writer dependant schemes. It is observed that the system achieves an accuracy of 92.84% and 96.24% respectively for the writer independant and writer dependant scheme considering top 3 choices.
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Chacko, A.M.M.O., Dhanya, P.M. (2015). Combining Classifiers for Offline Malayalam Character Recognition. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2. Advances in Intelligent Systems and Computing, vol 338. Springer, Cham. https://doi.org/10.1007/978-3-319-13731-5_3
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DOI: https://doi.org/10.1007/978-3-319-13731-5_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13730-8
Online ISBN: 978-3-319-13731-5
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