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

Rafi Hassan Chowdhury, Naimul Haque, Kaniz Fatiha

TL;DR

A novel, efficient and lightweight Vision Transformer model that can classify Bengali handwritten basic characters & digits effectively, addressing some of the shortcomings of traditional methods is proposed.

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%.

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

TL;DR

A novel, efficient and lightweight Vision Transformer model that can classify Bengali handwritten basic characters & digits effectively, addressing some of the shortcomings of traditional methods is proposed.

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%.
Paper Structure (14 sections, 6 figures, 6 tables)

This paper contains 14 sections, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Datasets
  5. Data Preprocessing
  6. Model Architecture
  7. Transfer Learning
  8. Experimental Setup
  9. Result Analysis
  10. Performance of different baseline architectures
  11. Class-wise Analysis
  12. Comparison with state-of-the-art methods
  13. Qualitative Analysis
  14. Conclusion & Future Work

Figures (6)

  • Figure 1: Sample of our Dataset
  • Figure 2: Sample of BanglaLekha-Isolated Dataset
  • Figure 3: Sample of augmentation techniques
  • Figure 4: GradCam samples of correctly classified inputs of BanglaLekha-Isolated
  • Figure 5: GradCam samples of correctly classified inputs of Bornomala
  • ...and 1 more figures