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With the increasing popularity of pen-based digital devices, online handwriting recognition has generated potential markets in India. Hand-held devices equipped with pen-based technologies are now affordable to a large section of Indian population. However, till date little research has been done on online handwriting recognition of an Indian script. On the other hand, India is a multilingual country and Bangla is its second most popular language used by nearly 220 million people in India and Bangladesh. Unconstrained handwriting in Bangla is cursive in nature unlike other Indian scripts. Difficulties of designing a recognition system for unconstrained Bangla handwriting are mainly due to the fact that Bangla has a very large alphabet set consisting of nearly 300 shapes many of which are very complex and also there are a large number of shape similar characters. In this article, we describe preliminary results of our recent study on limited vocabulary Bangla cursive handwriting recognition based on somewhat unusual combination of multilayer perceptron (MLP) and support vector machine (SVM) classifiers. We simulated the proposed approach on two vocabularies of sizes 50 and 110 and the recognition performance on these two are comparable.

Added on September 5, 2017

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  • Contributed by : OHWR Consortium
  • Product Type : Research Paper
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  • Author : Sk. Mohiuddin,Ujjwal Bhattacharya,Swapan K. Parui
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