Advancements in Handwritten Bangla Text Generation using Generative Adversarial Networks: A Focus on Complete Text Recovery

The advent of generative adversarial networks (GANs) has revolutionized the creation of handwritten characters.Nonetheless, there is still a shortfall in the generation of Bengali handwritten text.This research seeks to fill this gap by focusing on generating complete handwritten Bangla text, thus advancing the field of Bangla language processing.We showcase the effectiveness of our method in creating realistic handwritten Bangla text using GANs and the EKUSH dataset.By leveraging the interplay between GANs' generative and discriminative networks, we produce authentic handwritten Bangla text that meets practical needs in document digitization and language processing.Beyond the technical advancements, our study aims to enhance accessibility and inclusivity in Bangla language processing.We strive to bridge digital divides and promote literacy among Bangla-speaking communities by supporting individuals with limited access to technology or literacy resources.This paper highlights the potential and effectiveness of GANs in producing high-quality handwritten Bangla text, contributing to improvements in handwritten character recognition technology, and advancing various fields dependent on precise Bangla text processing.