Ink Meets Intelligence: A Computer Vision Based Approach for Bengali Handwritten Character Recognition

This research presents a practical and lightweight approach to Bengali handwritten character recognition. We developed an 84-class dataset containing 168,528 images of vowels, consonants, and compound characters by combining newly collected handwriting samples with the BanglaLekha-Isolated dataset. We also compared a custom CNN-MobileViT model with an adapted MobileNetV3-Small model. The best model achieved 94.71% test accuracy while using only 1.12 million parameters, making it suitable for devices with limited computational resources. In addition, we introduced a confusion-aware loss to give greater attention to visually similar characters and used Grad-CAM and LIME to explain which parts of each character influenced the model’s predictions.