An Ensemble Approach for Bangla Handwritten Character Recognition

Bangla is the sixth most widely spoken language worldwide, making the recognition of handwritten characters in this language an important task. However, recognizing Bangla characters is challenging due to the large number of characters in this language, which is around 400. Furthermore, with the increase in number of characters to recognize, the accuracy decreases. In this research, we propose a transfer learning architecture that branches pretrained convolutional neural network models VGG-16 and DenseNet-121, along with Nadam optimizer. The model is evaluated on Ekush dataset which contains 3,68,776 images over 122 characters or classes. Our proposed model achieved 96.94% accuracy on training data and 96.14% accuracy on test data. A comparison of the various state-of-the-art benchmarks for classifying Bangla Handwritten Characters demonstrates that the proposed model has outperformed previous studies. Our efforts will contribute to the development of an effective tool for automatically recognizing handwritten characters in Bangla.