PerSign: Personalized Bangladeshi Sign Letters Synthesis
This work addresses the communication gap between signers and non-signers in Bangladesh, though it appears incremental as it applies existing methods to a new domain-specific dataset.
The authors tackled the challenge of learning Bangladeshi Sign Language (BdSL) letters by proposing PerSign, a system that synthesizes personalized sign gestures into a person's image while preserving their appearance, using an image-to-image translation technique and a custom dataset.
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.