Sign Langauge Detection and Translation
Revolutionizing Sign Language Communication
Created on 9th March 2025
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Sign Langauge Detection and Translation
Revolutionizing Sign Language Communication
The problem Sign Langauge Detection and Translation solves
Problem Statement
Millions of people worldwide rely on sign language for communication, but many non-signers struggle to understand it. This language barrier limits interactions between deaf or hard-of-hearing individuals and the general public, making daily communication difficult in workplaces, educational institutions, healthcare, and social settings.
Existing solutions, such as human interpreters, are not always available, and text-based communication can be slow. This project provides an AI-powered real-time sign language recognition system that translates hand gestures into text and speech, making communication faster, more inclusive, and accessible.
How People Can Use It
✅ Bridging the Communication Gap
Converts sign language into spoken words in real-time, allowing deaf and hearing individuals to communicate seamlessly.
Supports multiple Indian languages, making it useful in multilingual environments.
✅ Enhancing Accessibility in Public Services
Helps in hospitals, government offices, and customer service centers where sign language interpreters are unavailable.
Improves accessibility for people with speech impairments by providing an alternative way to express themselves.
✅ Education & Learning
Assists teachers and students in classrooms, making education more inclusive for deaf students.
Helps hearing individuals learn sign language by providing gesture-to-text translations.
✅ Smart Assistive Technology
Can be integrated with robots, AI assistants, and smart devices to respond to sign language commands.
Useful for voice-controlled systems where speaking might not be possible.
Challenges we ran into
Building the Sign Language to Speech & Text Translation System came with several hurdles. Here are some of the key challenges we faced and how we overcame them:
🛑 1. Indian Accent Voice for Text-to-Speech (TTS)
Problem: The default voices available on the system were only US English voices, making it difficult to generate speech in an Indian accent.
Solution: We explored different TTS engines like Google Text-to-Speech (gTTS), Microsoft Azure Speech, and Amazon Polly, which support Indian-accented voices. We also tested open-source alternatives like Coqui TTS and Festival. Finally, we integrated gTTS, which provides a natural Indian accent and supports multiple Indian languages.
🛑 2. Accurate Hand Gesture Recognition with MediaPipe
Problem: The model sometimes misclassified hand signs, especially in low-light conditions or with partially visible hands.
Solution: We improved accuracy by:
Using data augmentation to train the model on different lighting conditions.
Implementing gesture smoothing to filter out accidental misclassifications.
Adjusting confidence thresholds to avoid incorrect predictions.
🛑 3. Asynchronous Translation & Speech Output
Problem: Translating sign language to text and then generating speech introduced delays, making real-time conversations difficult.
Solution: We optimized the pipeline using asynchronous programming (async/await) to process translation and speech output simultaneously. This improved response time and made interactions smoother.
🛑 4. Handling Short Forms & Sentence Recommendations
Problem: Users often sign short forms (e.g., "gm" for "good morning"), but direct translations didn’t make sense.
Solution: We built a short-form dictionary that automatically expands abbreviations before translation, ensuring meaningful text-to-speech output. We also added a recommendation system based on previously used sentences.