Features fusion based approach for handwritten Gujarati character recognition

Ankit Sharma, Priyank Thakkar, Dipak Adhyaru, Tanish Zaveri


Handwritten character recognition is a challenging area of research. Lots of research activities in the area of character recognition are already done for Indian languages such as Hindi, Bangla, Kannada, Tamil and Telugu. Literature review on handwritten character recognition indicates that in comparison with other Indian scripts research activities on Gujarati handwritten character recognition are very less.  This paper aims to bring Gujarati character recognition in attention. Recognition of isolated Gujarati handwritten characters is proposed using three different kinds of features and their fusion. Chain code based, zone based and projection profiles based features are utilized as individual features. One of the significant contribution of proposed work is towards the generation of large and representative dataset of 88,000 handwritten Gujarati characters. Experiments are carried out on this developed dataset. Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes (NB) classifier based methods are implemented for handwritten Gujarati character recognition. Experimental results show substantial enhancement over state-of-the-art and authenticate our proposals.


Gujarati handwritten numerals, Naive Bayes classification, Artificial Neural Networks, Support Vector Machine

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Copyright (c) 2017 Ankit Sharma, Priyank Thakkar, Dipak Adhyaru, Tanish Zaveri

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