Handwritten Telugu Compound Character Prediction using Convolutional Neural Network
Naresh Babu Muppalaneni
Abstract
Handwritten recognition is a challenging task for a long time either English or other language. Especially, Indian language handwritten letter is having many curves. It has been an open challenge for long time. In the advent of Machine Learning, the handwritten recognition becomes easier. However, many challenges still persist, because feature extraction is a difficult task since the characters are more in Indian languages. In this paper, we have chosen handwritten Telugu Compound Character called Guninthalu (Character formed with combination of Telugu Vowels and Consonants) for recognition. Since each letter in Telugu Guninthalu is almost similar to other. The classification is a challenging task. There are numerous machine learning techniques, however, the accuracy is the key challenge to achieve. Therefore, we deploy deep learning technique to enhance the accuracy which is observed test accuracy and training accuracy as high as 79.61% and 96.13% respectively. We have built a machine learning model with Convolutional Neural Network for Telugu Handwritten Gunithalu. We have created our own dataset and it is available in IEEE Dataport.(http://dx.doi.org/10.21227/phg6-m127).