EfficientNetV2S with End Ensemble for Robust Bangla Handwritten Character Recognition

Recognition of handwritten characters is an important task in the field of image processing. Bangla handwritten characters exhibit greater complexity and variation in shape, along with high inter-class similarity, making their recognition particularly challenging. To address this, we propose the “EfficientNetV2S” model, designed to recognize complex and visually similar characters in the Bangla language. Although Bangla is one of the most spoken languages in the world, research on Bangla character recognition is still comparatively limited. In this study, we apply a deep learning model trained and tested on a custom dataset of Bangla handwritten characters. The model is utilized both for feature extraction and for its proven efficiency in image classification tasks, enabling it to learn image features effectively and recognize characters with remarkable accuracy. Our approach achieves an impressive 96.63% accuracy, highlighting the strength and reliability of the proposed method. To broaden the range of classes, we combined the “BanglaLekha-Isolated” and “Matrivasa-raw (Ekush)” datasets. The results show that the End Ensemble technique effectively tackles recognition challenges and offers strong potential for real-world applications like automation and education. This research advances Bangla character recognition and encourages further exploration in the field.