# BeHGAN: Bengali Handwritten Word Generation from Plain Text Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2512.21694 · HTML: https://mlsanity.com/paper/2512.21694/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 25 Dec 2025.

## Builds on (most cited first, 15 of 15)

- [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://mlsanity.com/paper/2101.04061/) (2021)
- [Semi-Supervised Learning with Generative Adversarial Networks](https://mlsanity.com/paper/1606.01583/) (2016)
- [ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation](https://mlsanity.com/paper/2003.10557/) (2020)
- [GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images](https://mlsanity.com/paper/2003.02567/) (2020)
- [Geometry Score: A Method For Comparing Generative Adversarial Networks](https://mlsanity.com/paper/1802.02664/) (2018)
- [DeepWriting: Making Digital Ink Editable via Deep Generative Modeling](https://mlsanity.com/paper/1801.08379/) (2018)
- [Handwriting Transformers](https://mlsanity.com/paper/2104.03964/) (2021)
- [Handwritten Text Generation from Visual Archetypes](https://mlsanity.com/paper/2303.15269/) (2023)
- [SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text](https://mlsanity.com/paper/2202.11456/) (2022)
- [Generating Handwriting via Decoupled Style Descriptors](https://mlsanity.com/paper/2008.11354/) (2020)
- [VATr++: Choose Your Words Wisely for Handwritten Text Generation](https://mlsanity.com/paper/2402.10798/) (2024)
- [Generative Adversarial Network for Handwritten Text](https://mlsanity.com/paper/1907.11845/) (2019)
- [BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset](https://mlsanity.com/paper/1703.10661/) (2017)
- [Handwriting Profiling using Generative Adversarial Networks](https://mlsanity.com/paper/1611.08789/) (2016)
- [Bangla Handwritten Digit Recognition and Generation](https://mlsanity.com/paper/2103.07905/) (2021)

## Abstract (arXiv)

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.