Telugu handwritten character recognition using deep residual learning

Present years are the exciting times for recognition of handwritten characters in the fields of Image Processing, Pattern Recognition, and Computer Vision. Recognizing handwritten characters using deep convolutional neural networks is a new era. There are various techniques available for handwritten recognition of characters, depending on hand-designed features. The proposed work is based on a systematic method to recognize both offline and online Telugu handwritten characters with residual learning framework called ResNet. A residual learning network is a concept of deeper neural networks where the training of the data is more effective. ResNet enables building very deep networks by addressing the vanishing gradient problem that occurs in deep convolutional neural networks. This paper deals in developing a fast, reliable Telugu handwritten ResNet for both online and offline character recognition and also improves the classification performance. The model is evaluated with IIITS-Telugu Handwriting Database; HP Labs database (Telugu) India and achieved very promising results. The Proposed residual net (ResNet-50) achieves 2.37% error on the ResNet-18 & 34 test set.