# PerSign: Personalized Bangladeshi Sign Letters Synthesis

arXiv: https://arxiv.org/abs/2209.14591 · HTML: https://mlsanity.com/paper/2209.14591/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [GestureGAN for Hand Gesture-to-Gesture Translation in the Wild](https://mlsanity.com/paper/1808.04859/) (2018)
- [BdSL36: A Dataset for Bangladeshi Sign Letters Recognition](https://mlsanity.com/paper/2110.00869/) (2021)
- [Real Time Bangladeshi Sign Language Detection using Faster R-CNN](https://mlsanity.com/paper/1811.12813/) (2018)

## Abstract (arXiv)

Bangladeshi Sign Language (BdSL) - like other sign languages - is tough to learn for general people, especially when it comes to expressing letters. In this poster, we propose PerSign, a system that can reproduce a person's image by introducing sign gestures in it. We make this operation personalized, which means the generated image keeps the person's initial image profile - face, skin tone, attire, background - unchanged while altering the hand, palm, and finger positions appropriately. We use an image-to-image translation technique and build a corresponding unique dataset to accomplish the task. We believe the translated image can reduce the communication gap between signers (person who uses sign language) and non-signers without having prior knowledge of BdSL.