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There are many scripts in the world, several of which are used by hundreds of millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. However, convolutional neural network (CNN) has recently been used as an efficient unsupervised feature vector extractor. Although such a network can be used as a unified framework for both feature extraction and classification, it is more efficient as a feature extractor than as a classifier. In the present study, we performed certain amount of training of as-layer CNN for a moderately large class character recognition problem. We used this CNN trained for a larger class recognition problem towards feature extraction of samples of several smaller class recognition problems. In each case, a distinct Support Vector Machine (SVM) was used as the corresponding classifier. In particular, the CNN of the present study is trained using samples of a standard 50-class BangIa basic character database and features have been extracted for 5 different lO-c1ass numeral recognition problems of English, Devanagari, BangIa, Telugu and Oriya each of which is an official Indian script. Recognition accuracies are comparable with the state-of-the-art.

Added on September 4, 2017

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  • Contributed by : OHWR Consortium
  • Product Type : Research Paper
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  • Author : Durjoy Sen Maitra,Ujjwal Bhattacharya,Swapan K. Parui
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