BornoViT: A Novel Efficient Vision Transformer for Bengali Handwritten Basic Characters Classification

TL;DR

BornoViT is a lightweight, efficient Vision Transformer designed for Bengali handwritten character classification, achieving high accuracy with minimal computational resources, suitable for resource-limited environments.

Contribution

The paper introduces BornoViT, a novel compact Vision Transformer that outperforms existing models in Bengali handwritten character recognition while significantly reducing computational complexity.

Findings

Achieved 95.77% accuracy on BanglaLekha dataset.

Model size of 0.62 MB with 0.16 GFLOPs.

Superior efficiency compared to state-of-the-art models.

Abstract

Handwritten character classification in the Bengali script is a significant challenge due to the complexity and variability of the characters. The models commonly used for classification are often computationally expensive and data-hungry, making them unsuitable for resource-limited languages such as Bengali. In this experiment, we propose a novel, efficient, and lightweight Vision Transformer model that effectively classifies Bengali handwritten basic characters and digits, addressing several shortcomings of traditional methods. The proposed solution utilizes a deep convolutional neural network (DCNN) in a more simplified manner compared to traditional DCNN architectures, with the aim of reducing computational burden. With only 0.65 million parameters, a model size of 0.62 MB, and 0.16 GFLOPs, our model, BornoViT, is significantly lighter than current state-of-the-art models, making it more suitable for resource-limited environments, which is essential for Bengali handwritten character classification. BornoViT was evaluated on the BanglaLekha Isolated dataset, achieving an accuracy of 95.77%, and demonstrating superior efficiency compared to existing state-of-the-art approaches. Furthermore, the model was evaluated on our self-collected dataset, Bornomala, consisting of approximately 222 samples from different age groups, where it achieved an accuracy of 91.51%.