Recognition of Handwritten Numerals of various Indian Regional Languages using Deep Learning
Saumya Chaurasia, Suneeta Agarwal · 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · 2018
Handwritten numeral recognition is an interesting area of research in the field of computer vision and pattern recognition. It plays an important role in postal automation services especially in a country like India where multiple languages and scripts are used. So, the recognition system needs to deal with many challenges like varying writing styles and cursive nature of handwriting. This paper proposes an effective handwritten numeral recognition approach based on Convolutional Neural Network (CNN) and Support Vector Machine (SVM). The proposed work is an attempt to develop a recognition system for recognizing the handwritten digits written in any one of the regional languages: Bangla, Devanagari, Oriya, and Telugu. The proposed system first normalizes the input image having single digit thereafter CNN works as a feature extractor while SVM as a classifier. Experiments have been conducted on benchmark database of ISI Kolkata (having numerals of Bangla, Devanagari, and Oriya languages) and CMaterdb (having numerals of Telugu language) (Jadavpur University). The results show that our model performs best for Devanagari language with accuracy 99.41% and for Bangla, Telugu, and Oriya, the accuracies are 99.14%, 99.16%, and 94.54% respectively. So, the performance of the proposed approach is better than state-of-art approaches.