Title: Gujarati handwritten numeral recognition through fusion of features and machine learning techniques
Authors: Ankit K. Sharma; Priyank Thakkar; Dipak M. Adhyaru; Tanish H. Zaveri
Addresses: Institute of Technology, Nirma University, Ahmedabad, Gujarat, India ' Institute of Technology, Nirma University, Ahmedabad, Gujarat, India ' Institute of Technology, Nirma University, Ahmedabad, Gujarat, India ' Institute of Technology, Nirma University, Ahmedabad, Gujarat, India
Abstract: Languages have played a major role in Indian history and they continue to influence the lives of Indians till date. Plentiful research on optical character recognition (OCR) techniques for Indian languages such as Hindi, Tamil, Bangla, Kannada, Gurumukhi, Malayalam and Marathi has already been carried out. Research efforts on Gujarati character recognition are few and yet to gain momentum. This paper intends to bring Gujarati character recognition in attention. Methods based on artificial neural network (ANN), support vector machine (SVM) and naive Bayes (NB) classifier are exercised for handwritten Gujarati numerals recognition. Experiments are carried out on two large datasets using three different kinds of features and their fusion. Zone-based, projection profiles-based and chain code-based features are employed as individual features. The paper proposes to use a fusion of these features for learning prediction models. Experimental results show significant improvement over state-of-the-art and validate our proposals.
Keywords: Gujarati handwriting; Gujarati numerals; handwritten numerals; features; machine learning; handwritten numeral recognition; naive Bayes classification; artificial neural networks; ANNs; support vector machines; SVM; India; optical character recognition; OCR; Gujarati character recognition.
International Journal of Computational Systems Engineering, 2017 Vol.3 No.1/2, pp.35 - 47
Available online: 20 Mar 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article