Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach

Recognising the Bangla alphabet remains a significant challenge within the fields of computational linguistics and artificial intelligence, primarily due to the script’s inherent structural complexity and wide variability in writing styles. The Bangla script is characterised by intricate ligatures, overlapping diacritics, and visually similar graphemes, all of which complicate automated recognition tasks. Despite ongoing advancements in deep learning (DL), machine learning (ML), and image processing (IP), accurately identifying Bangla characters continues to be a demanding and unresolved issue. A key limitation lies in the absence of robust detection frameworks capable of accommodating the script’s complex visual patterns and nuances. To address this gap, we propose an enhanced object detection model based on the YOLOv11 architecture, incorporating a ResNet50 backbone for improved feature extraction. The YOLOv11 framework is particularly effective in capturing discriminative features from input images, enabling real-time detection with high precision. This is especially beneficial in overcoming challenges such as character overlap and stylistic diversity, which often hinder conventional recognition techniques. Our approach was evaluated on a custom dataset comprising 50 primary Bangla characters (including vowels and consonants) along with 10 numerical digits. The proposed model achieved a recognition confidence of 99.9%, markedly outperforming existing methods in terms of accuracy and robustness. This work underscores the potential of single-shot detection models for the recognition of complex scripts such as Bangla. Beyond its technical contributions, the model has practical implications in areas including the digitisation of historical documents, the development of educational tools, and the advancement of inclusive multilingual technologies. By effectively addressing the unique challenges posed by the Bangla script, this research contributes meaningfully to both computational linguistics and the preservation of linguistic heritage.