A squeeze and excitation ResNeXt-based deep learning model for Bangla handwritten compound character recognition

In today's digital era, the recognition of handwritten documents is highly demanding due to the widespread applications. Bangla is one of the most used languages in the world, which consists of 50 basic alphabets. Many compound characters exist in Bangla, which is the combination of two or more basic characters. The recognition of Bangla handwritten characters is a challenging task due to their various sizes, sheer, diversity, lots of turns, similarities between alphabets, and different writing patterns. This paper presents a deep CNN (Convolutional Neural Network) model using the SE-ResNeXt. The squeeze and excitation (SE) blocks are added with existing ResNeXt to address the Bangla handwritten compound character recognition. Usually, CNN extracted the spatial features in the lower-layers and complex features in the upper layers. The SE blocks are added to improve the performance of usual deep CNN by automatically fusing the channel-wise spatial information and inter-channel dependencies through squeezing and excitation within local receptive fields, respectively. To validate the performance of the proposed model, we have utilized Mendeley BanglaLekha-Isolated 2 dataset. The experimental results demonstrate that the proposed model exhibits an average accuracy of 99.82% in recognizing Bangla handwritten compound characters. In addition, the proposed model outperforms the state-of-the-art models by demonstrating higher results. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper presents a deep CNN model using SE-ResNeXt to recognize Bangla handwritten compound characters, achieving an average accuracy of 99.82% and outperforming state-of-the-art models in the experiment.