Hindi Handwritten Character Recognition using CNN
This paper focuses on the task of recognizing handwritten Hindi characters using a Convolutional Neural Network (CNN) based Deep Learning model. The recognized characters can then be stored digitally in the computer or used for other purposes. The dataset used is obtained from the UC Irvine Machine Learning Repository which contains 92,000 images divided into training (85%) and test set (15%). It contains different forms of handwritten Devanagari characters written by different individuals which can be used to train and test handwritten text recognizers and perform writer identification and verification experiments. The model is implemented using Keras libraries on top of a TensorFlow backend. It contains four CNN layers followed by three fully connected layers for recognition. Grayscale handwritten character images are used as input. Filters are applied on the images to extract different features at each layer. This is done by the Convolution operation. The two other main operations involved are Pooling and Flattening. The output of the CNN layers are fed to the fully connected layers. Finally, the chance or probability score of each character is determined and the character with the highest probability score is shown as the output. A recognition accuracy of 98.94% is obtained. Similar models exist for the purpose, but the proposed model achieved a better performance and accuracy than some of the earlier models.