Abstract
In this paper, we propose a new approach for Odia handwritten character recognition based on discrete wavelet transform (DWT) and principal component analysis (PCA). Statistical feature descriptors like mean, standard deviation, energy have been computed from each sub-band of the second level DWT and are served as the primary features. To find the most significant features, PCA is applied. Subsequently, back-propagation neural network (BPNN) is harnessed to perform the classification task. The proposed method is validated on a standard Odia dataset, containing 150 samples from each of the 47 categories. The simulation results offer a recognition rate of 94.8%.
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Sethy, A., Patra, P.K., Nayak, D.R. (2018). Off-Line Handwritten Odia Character Recognition Using DWT and PCA. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-10-6872-0_18
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DOI: https://doi.org/10.1007/978-981-10-6872-0_18
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