SIGNOPSIS
Hands Speak. We Translate.
The problem SIGNOPSIS solves
The problem SIGNOPSIS solves :
There is a significant communication gap between people who use sign language for communication and the general public, as most people do not understand sign language. This gap makes everyday interactions in places such as classrooms, workplaces, hospitals, and public services difficult and often dependent on interpreters.
Existing solutions are usually limited to isolated sign recognition, require special hardware, or do not work reliably in real‑time continuous scenarios. They fail to support natural, uninterrupted signing, which is how sign language is actually used in real life.
Our system addresses this problem by enabling real‑time translation of continuous sign language into readable text using only a standard webcam. The system automatically detects sign boundaries, recognizes words from continuous motion, and constructs meaningful sentences without requiring pauses, buttons, or external devices.
By bridging communication between people who use sign language for communication and everyone else, this solution makes interactions more accessible, practical, and inclusive in real‑world settings.
Challenges we ran into
Specific bugs and challenges we faced, and how we solved them
One of the major challenges we faced while building this project was handling continuous sign language input. Initially, the model worked well for isolated signs, but during live usage it repeatedly predicted the same word multiple times or failed to recognize when one sign ended and the next began. This made it difficult to form meaningful sentences from natural, uninterrupted signing.
To overcome this, we shifted our focus from only improving model accuracy to designing temporal decoding logic. We introduced motion‑based detection to identify when a sign starts and stops by analyzing changes in hand landmark movement. We then implemented buffering and smoothing techniques to collect multiple predictions during motion and select the most stable one, reducing noise and duplicate outputs.
Another challenge was ensuring the system worked reliably in real time with limited computational resources. Instead of using heavy pre‑trained video models, we optimized our pipeline using lightweight hand‑landmark features and a BiLSTM model, which allowed us to achieve stable performance with a standard webcam.
Through iterative debugging, extensive testing, and refining both the model and the decoding logic, we successfully transitioned from isolated sign recognition to real‑time continuous sign‑to‑sentence translation, which was the most critical hurdle in the project.