cs.CVMar 3, 2026

Maximizing Generalization: The Effect of Different Augmentation Techniques on Lightweight Vision Transformer for Bengali Character Classification

Authors: Rafi Hassan Chowdhury, Naimul Haque, Kaniz Fatiha

Organizations: Department of Computer Science and Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh

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Abstract

Deep learning models have proven to be highly effective in computer vision, with deep convolutional neural networks achieving impressive results across various computer vision tasks. However, these models rely heavily on large datasets to avoid overfitting. When a model learns features with either low or high variance, it can lead to underfitting or overfitting on the training data. Unfortunately, large-scale datasets may not be available in many domains, particularly for resource-limited languages such as Bengali. In this experiment, a series of tests were conducted in the field of image data augmentation as an approach to addressing the limited data problem for Bengali handwritten characters. The study also provides an in-depth analysis of the performance of different augmentation techniques. Data augmentation refers to a set of techniques applied to data to increase its size and diversity, making it more suitable for training deep learning models. The image augmentation techniques evaluated in this study include CLAHE, Random Rotation, Random Affine, Color Jitter, and their combinations. The study further explores the use of augmentation methods with a lightweight model such as EfficientViT. Among the different augmentation strategies, the combination of Random Affine and Color Jitter produced the best accuracy on the Ekush [1] and AIBangla [2] datasets, achieving accuracies of 97.48% and 97.57%, respectively. This combination outperformed all other individual and combined augmentation techniques. Overall, this analysis presents a thorough examination of the impact of image data augmentation in resource-scarce languages, particularly in the context of Bengali handwritten character recognition using lightweight models.

Figures & tables

Fig. 1: Handwritten Bangla Basic Characters and Numerals from Ekush Dataset
Fig. 2: Handwritten Bangla Basic Characters from AI-bangla Dataset
Fig. 3: Sample of augmentation techniques
Augmentation TechniqueAIBangla_Basic
precisionrecallf1-scoreaccuracy
None96%96%96%96.36%
RandomRotation (RR)97%97%97%96.92%
RandomAffine (RA)97%97%97%97.28%
CLAHE (C)97%97%97%96.51%
ColorJitter (CJ)97%97%97%96.65%
TABLE II: Comparison of Augmentation Techniques for AIBangla_Basic Dataset using EfficientViT
Augmentation TechniqueEkush-Bangla
precisionrecallf1-scoreaccuracy
None97%97%97%97.23%
RandomRotation (RR)97%97%97%97.22%
RandomAffine (RA)97%97%97%97.45%
CLAHE (C)97%97%97%97.24%
ColorJitter (CJ)97%97%97%97.34%
TABLE III: Comparison of Augmentation Techniques for Ekush-Bangla Dataset using EfficientViT
ArchitectureAccuracy (%)
DConvAENNet [ 18 ]95.53%
CNN [ 19 ]95.49%
Ours97.48%
TABLE V: Comparison with state-of-the-art methods on Ekush Dataset
Fig. 4: GradCam samples of correctly classified inputs
Fig. 5: Misclassified samples that are visually similar to the predicted class.

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