A Hybrid Approach for Denoising and Recognition of Handwritten Characters Using Deblur GAN-CNN

Handwritten Character Recognition (HCR) is a critical area of research within pattern recognition and artificial intelligence, with applications spanning from document digitization, optical character recognition (OCR), and handwriting analysis. Despite significant technological advancements, accurate recognition of handwritten characters remains challenging due to variations in writing styles, sizes, and inherent distortions.This paper presents a novel approach to HCR, utilizing a combination of Deblur Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN) to effectively address these challenges. Unlike traditional deblurring methods, which often introduce artifacts or blur the edges of characters, Deblur GAN is specifically trained to enhance the clarity of handwritten images while preserving essential character features. This allows the subsequent CNN to extract more accurate and discriminative features, leading to improved character recognition.Through extensive experimentation on benchmark datasets such as MNIST, an approach that significantly outperforms the existing methods is demonstrated in terms of recognition accuracy, particularly in scenarios involving blurred or indistinct handwriting. These findings highlight the potential of integrating deblurring techniques with deep learning models to address persistent challenges in handwritten character recognition, paving the way for enhanced automation and efficiency in text analysis tasks.