Odia character recognition using backpropagation network with binary features
Mamata Nayak, Ajit Kumar Nayak · International Journal of Computational Vision and Robotics · 2017
Automatic recognition of printed characters has been an area of active research since last few decades. Though considerable work has been reported for Latin, CJK, and many other popular languages, but a few works has been reported for many Indian languages. In this work, we propose a complete model with effective techniques to recognise printed offline Odia language characters. First we develop a technique to segment the image to extract characters, and then features of these segmented characters are extracted using a binary feature extraction, as well as structural feature extraction. Finally, these extracted features are used to classify character symbols using a modified backpropagation network. In each of these phases it is found that the proposed technique outperforms existing techniques and yields high accuracy rate. Further, this work has tried to include all the possible characters (approx. 2,500 characters) in training/testing unlike other works, where a small set of characters are normally considered for testing.