Online Bengali Handwritten Numerals Recognition Using Deep Autoencoders

Pal, A and Khonglah, B K and Mandal, S and Choudhury, H and Prasanna, S R M and Rufiner, H L and Balasubramanian, Vineeth N (2016) Online Bengali Handwritten Numerals Recognition Using Deep Autoencoders. In: 22nd National Conference on Communications (NCC), MAR 04-06, 2016, Guwahati, INDIA.

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Abstract

This work describes the development of online handwritten isolated Bengali numerals using Deep Autoencoder (DA) based on Multilayer perceptron (MLP) [1]. Autoencoders capture the class specific information and the deep version uses many hidden layers and a final classification layer to accomplish this. DA based on MLP uses the MLP training approach for its training. Different configurations of the DA are examined to find the best DA classifier. Then an optimization technique have been adopted to reduce the overall weight space of the DA based on MLP that in turn makes it suitable for a real time application. The performance of the DA based system is compared with systems constructed using Hidden Markov Model (HMM) and Support Vector Machine (SVM). The confusion matrices of DA, HMM and SVM are analyzed in order to make a hybrid numeral recognizer system. It is found that hybrid system gives better performance than each of the individual systems, where the average recognition performances of DA, HMM and SVM systems are 97.74 %, 97.5 % and 98.14 %, respectively and hybrid system gives a performance of 99.18 %.

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