Deep-Sign: An Open-Source Project for Real-Time Conversion of Audio-Video to Sign Language Using AI

Deep-Sign is an innovative open-source AI system whose core goal is to break down communication barriers between the deaf community and the spoken language world. The project uses Google Gemini 3.1 multimodal model to extract audio from videos and convert it to text, then programmatically maps it to standard sign language videos, helping the deaf community access video content conveniently and promoting digital inclusion practices.

About 70 million deaf people worldwide use sign language as their primary means of communication, but most video content is difficult for them to access. The Deep-Sign project emerged to address this, aiming to use AI technology to bridge the information gap between sign language and spoken language, lower the barrier for content creators to provide accessible services, and serve as an important practice in digital inclusion.

Deep-Sign adopts a modular two-stage architecture:

Audio-to-Text Conversion

It uses Google Gemini 3.1 multimodal model to extract audio from videos and convert it to text. Compared to traditional pipelines, it has higher accuracy and can better handle scenarios like accents and background noise.

Text-to-Sign Language Mapping

Based on a library of pre-recorded standard sign language video clips, it intelligently matches and splices the corresponding sign language clips for the text, outputting a coherent video.

Advantages of the hybrid architecture: High accuracy (avoids non-standard gestures generated by AI), fast response, strong maintainability (sign language library can be updated independently), and resource-friendly (low computing requirements).

Deep-Sign can be applied in multiple scenarios:

• Education: Automatically generate sign language versions of online courses to help deaf students access knowledge equally;
• Public Services: Real-time/near-real-time sign language conversion for public information such as government announcements and hospital guidelines;
• Media Communication: News agencies generate sign language versions for video news to expand their audience;
• Corporate Communication: Accessibility transformation of content like corporate training and product introductions.

The core innovation of the project lies in balancing AI capabilities and engineering practicality: instead of blindly pursuing pure AI generation, it adopts an "AI + programmatic" hybrid solution. Gemini 3.1 ensures the accuracy of speech recognition, while programmatic mapping guarantees the standardization and fluency of sign language. The layered architecture reserves expansion space, such as integrating multilingual sign language libraries and personalized gesture styles.

As an open-source project, Deep-Sign provides a complete reference implementation of AI-assisted accessibility technology, which can serve as a learning case for multimodal AI applications and video processing pipelines. Enthusiasts from the deaf community can participate in expanding and optimizing the sign language video library, forming a positive cycle of technology empowerment and community co-construction.

Deep-Sign demonstrates the potential of AI in the field of social welfare, and its value lies in solving the real communication problems of the deaf community. With the improvement of multimodal model capabilities and the enrichment of sign language libraries, such AI-assisted communication tools are expected to become standard configurations of digital infrastructure, turning information accessibility from an ideal into reality.