Handwritten Kannada Character Recognition Using Convolutional Neural Networks and Transfer Learning

Abstract

Abstract Handwritten Kannada Character Recognition is special field of pattern recognition as different human being will have their own handwritten styles, machine considers even noise as a pattern. In order to classify each character, we present Convolutional Neural Networks and Transfer Learning techniques. Here we use 74k dataset and labelling individual character as one class totally we had made 25 classes. Initially we pre-process each image of character to remove noise, convert to gray-scale, applied gaussian blur and canny edge detection and cropped the images and at last resizing it. These methods help in training neural networks to recognize the characters with good accuracy. Then we apply transfer learning to same dataset, here we resize image into three-dimensional and convert images into gray-scale and using xception we train the model. Even though a few of the kannada language’s characters have similarities, this model can accurately categorise each character to the appropriate class and identify it with better accuracy than CNN.