Recognition of Online Handwritten Bangla and Devanagari Basic Characters: A Transfer Learning Approach

The transfer learning approach has eradicated the need for running the Convolutional Neural Network (CNN) models from scratch by using a pre-trained model with pre-set weights and biases for recognition of different complex patterns. Going by the recent trend, in this work, we have explored the transfer learning approach to recognize online handwritten Bangla and Devanagari basic characters. The transfer learning models considered here are VGG-16, ResNet50, and Inception-V3. To impose some external challenges to the models, we have augmented the training datasets by adding different complexities to the input data. We have also trained these three transfer learning models from scratch (i.e., not using pre-set weights of the pre-trained models) for the same recognition tasks. Besides, we have compared the outcomes of both the procedures (i.e., running from scratch and by using pre-trained models). Results obtained by the models are promising, thereby establishing its effectiveness in developing a comprehensive online handwriting recognition system.