CompoundDenseNet: a novel approach for accurate recognition of Bangla handwritten compound characters

Bangla, one of the most widely spoken languages in the world, presents major challenges in handwritten character recognition because of its complex compound characters with intricate shapes, diverse writing styles, and structural similarities. These features make Bangla a representative example of complex scripts that remain difficult for conventional Optical Character Recognition (OCR) systems. This study focuses on improving the recognition of Bangla handwritten compound characters using a modified DenseNet architecture named CompoundDenseNet. The architecture enhances feature extraction and reuse to better capture the visual variations and fine structural details that existing models often struggle to handle. Its performance was evaluated on three benchmark datasets, BanglaLekha Isolated, Ekush, and CMATERdb, achieving recognition accuracies of 98.5%, 98%, and 96.2% respectively, surpassing previously…