Bengali Handwritten Character Recognition Enhanced with Morphological Preprocessing and Attention Mechanism-based Convolutional Neural Network

Recognizing handwritten characters presents significant challenges, particularly in languages like Bengali, which have numerous characters with their own styles and shapes.To address this challenge, a comprehensive approach to handwriting character recognition (HCR) in Bengali is proposed that utilizes convolutional neural networks (CNNs) enhanced with upsampling layers, residual blocks, and efficient channel attention.The technique leverages morphological preprocessing, like opening and closing in the preprocessing stage, to improve the model's ability to distinguish between visually identical characters.The opening and closing techniques are applied to one character and another between two characters with almost identical shapes, thereby enabling the model to discriminate between characters with similar shapes.The suggested methodology is effective, achieving a noteworthy accuracy of 96.29% on the Banglalekha isolated dataset of 84 classes.Additionally, the Efficient Channel Attention(ECA) mechanism greatly improves accuracy from 96.09% to 96.29% on the recognition challenge by allowing the model to concentrate on discriminative features.Overall, the incorporation of Efficient Channel Attention mechanisms and morphological preprocessing significantly improve the model's capacity to recognize Bengali characters.The suggested method can address various practical challenges