SignVerse
Where Silence Speaks, and Voices Connect
The problem SignVerse solves
our project solves the problem of communication barriers for deaf and mute users, especially in India where Indian Sign Language (ISL) is under-supported by mainstream technology.
Most existing solutions only recognize isolated signs (letters or words), which makes conversations slow and unnatural. Your platform goes beyond that by enabling real-time, sentence-level translation between text, sign, and speech, along with features like video calling with live sign-to-text, text-to-sign for remote communication, learning mode, and quiz-based engagement.
In simple terms: it helps deaf and mute users communicate fluidly with both signers and non-signers, while also learning and practicing ISL interactively, bridging the gap between accessibility and everyday communication.
Challenges we ran into
One of the biggest hurdles we ran into was real-time sentence-level sign detection. Initially, our model was misclassifying gestures when users signed quickly or in poor lighting, which led to broken or incorrect sentences. This was a major issue because our platform’s core promise is accurate and fluid communication.
To overcome this, we introduced a sequence-based buffer system: instead of predicting every frame independently, we grouped gestures into short sequences and applied smoothing algorithms with context-based correction. We also fine-tuned our model with augmented data (low light, different angles, varying speeds) to improve robustness.
This combination greatly reduced false predictions, allowed smoother translations, and made the interaction much closer to natural communication.