EfficientNetV2S with End Ensemble for Robust Bangla Handwritten Character Recognition

Recognizing handwritten characters continues to be an important challenge within the domain of image processing. Specifically, Bangla handwritten characters represent a difficult challenge due to their complex shapes, variation, and high interclass similarity. We introduce the EfficientNetV2S model to overcome the difficulties. It is adapted specifically to recognize the complex and visually similar characters in the Bangla language. Research on Bangla handwritten characters is still limited, although it is the world’s 7th most spoken language. This study presents a deep learning approach that was trained and tested on a dataset containing handwritten Bangla character samples. The model can properly extract the features of the images and classify the images correctly with high accuracy. This approach enabled the model to learn complex features of the images and achieve remarkable character recognition accuracy. We have increased the number of categories of the different characters by combining the “BanglaLekha-Isolated” and “Matrivasa-raw (Ekush)” datasets, which are used for training our model. Our
method achieves an impressive 96.63% accuracy, which proves our proposed technique works very effectively and can be trusted. The results confirm that the end-ensemble technique solves recognition challenges accurately. Our technique can offer strong potential for real-world applications such as automation and education. This work significantly advances the field of Bangla character recognition and encourages further exploration.