Signapse

🚀 About the Project Signapse is an AI-powered real-time sign language translation system built to break down communication barriers between the hearing and hearing-impaired communities. Our mission is to foster inclusivity through technology by enabling seamless two-way communication—translating sign language to text/audio and vice versa.

💡 What Inspired Us The idea for Signapse was born out of a simple observation: millions of people around the world who are deaf or hard of hearing face communication challenges in everyday life. Whether it’s at schools, hospitals, public offices, or workplaces, the lack of accessible communication tools often leaves them behind. We were driven by the vision of a world where technology bridges this gap and empowers everyone to communicate freely.

🛠️ How We Built It Our tech stack combined computer vision, deep learning, and natural language processing:

Model Training: We trained a custom gesture recognition model using YOLOv5 and MediaPipe for real-time hand detection.

Translation Engine: For sign-to-text, we used sequential recognition models on labeled gesture datasets. For text-to-sign, we implemented a video-based sign language rendering engine.

Speech Integration: Integrated gTTS for converting recognized signs into natural speech.

Frontend & Interface: A user-friendly GUI was developed using Tkinter and OpenCV for live video processing and feedback.

Voice Input Mode: We added voice-to-sign capability, allowing spoken input to be translated into sign visuals using sign video snippets.

🧠 What We Learned Real-time gesture detection is complex and requires careful optimization to balance speed and accuracy.

Creating smooth and synchronized sign animations for full sentences is much harder than detecting isolated signs.

Accessibility solutions must be robust, reliable, and work well across diverse conditions like lighting and background noise.

Team collaboration and version control were key to integrating multiple AI components efficiently.

⚙️ Challenges We Faced Model Accuracy: Fine-tuning the model to differentiate between similar signs.

Synchronization: Ensuring that sign language video playback aligned precisely with real-time detection.

Dataset Limitations: Lack of large, diverse sign language datasets for continuous sentence detection.

Real-Time Performance: Optimizing the system to run smoothly on consumer-grade hardware without GPU support.

🌍 Impact & Vision We believe Signapse is more than a tool—it's a movement toward a more inclusive society. We envision this evolving into a wearable device or mobile application that enables effortless communication anywhere, anytime.