Abstract

The recognition of handwritten Bengali characters presents significant challenges due to the script’s morphological complexity, including visually similar conjunct characters and subtle distinguishing features such as dots or lines, compounded by variations in handwriting styles. To address these challenges, an efficient transfer learning framework is proposed for Bengali Handwritten Character Recognition (BHCR), leveraging advanced deep learning methodologies. A pretrained ResNet-50 model, originally trained on the ImageNet dataset, is fine-tuned with the One Cycle Policy for cyclic learning rate optimization to expedite convergence. Evaluation on the Ekush dataset, comprising 367,018 isolated handwritten characters, demonstrates that the proposed method achieves an accuracy of 95.78% after 50 epochs, surpassing many comparable techniques in efficiency while minimizing architectural overhead.

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