Bangla Handwritten Digit Recognition Approach with an Ensemble of Deep Residual Networks

This work presents an Xception ensemble network based Bangla handwritten digit classification scheme. Bangla handwritten digits are challenging to recognize due to some strong similar features between different classes. In this study, heavy augmentation has been used in the training set along with dropout in the model to avoid overfitting. Competitive performance has been achieved with optimized number of model parameters. An ensemble of three Xception networks was evaluated on a hidden test set where it showed promising performance of 96.69% accuracy, F1 score of 97.14%.