BeHGAN: Bengali Handwritten Word Generation from Plain Text Using Generative Adversarial Networks
This work addresses the lack of resources for Bengali handwritten text generation, which is incremental as it applies existing GAN methods to a new language-specific domain.
The authors tackled the problem of generating Bengali handwritten words from plain text, a task with limited prior research, and demonstrated the ability to produce diverse outputs using a self-collected dataset from about 500 individuals.
Handwritten Text Recognition (HTR) is a well-established research area. In contrast, Handwritten Text Generation (HTG) is an emerging field with significant potential. This task is challenging due to the variation in individual handwriting styles. A large and diverse dataset is required to generate realistic handwritten text. However, such datasets are difficult to collect and are not readily available. Bengali is the fifth most spoken language in the world. While several studies exist for languages such as English and Arabic, Bengali handwritten text generation has received little attention. To address this gap, we propose a method for generating Bengali handwritten words. We developed and used a self-collected dataset of Bengali handwriting samples. The dataset includes contributions from approximately five hundred individuals across different ages and genders. All images were pre-processed to ensure consistency and quality. Our approach demonstrates the ability to produce diverse handwritten outputs from input plain text. We believe this work contributes to the advancement of Bengali handwriting generation and can support further research in this area.