AI Accessibility and Communication Toolkit

[A1L13N]

Description: This project focuses on using AI to break communication barriers and assist people with disabilities. A prime example is an AI-driven sign language translator that can interpret sign language into spoken/written language and vice versa. Using computer vision, the system would track a person’s hand gestures via a camera and an AI model would translate those signs into real-time text or speech . Conversely, it could take written text or speech and generate a responsive avatar or animation that signs in a chosen sign language for deaf users. Beyond sign language, the toolkit could include other accessibility features: for instance, image recognition to narrate the environment to blind users, or speech simplification for people with cognitive disabilities. The core idea is leveraging modern AI (especially vision and language models) to create assistive tools that operate in real time, enabling more inclusive communication.

Core Features:
• Sign-to-Speech Translator: A camera-based AI that recognizes sign language gestures (using a trained neural network) and outputs spoken words or text. This could support multiple sign languages (ASL, BSL, etc.).
• Speech/Text-to-Sign: An animated digital avatar that signs messages to a deaf user. The system uses an NLP model to ensure the spoken language is properly translated into grammatically correct sign language sequences.
• Visual Describer: For visually impaired users, an AI vision module that can describe the scene or read out text from images (like signs or menus) when they point their phone camera – essentially combining image recognition with natural language generation.
• Live Captioning & Translation: Real-time transcription of speech to text (for those hard of hearing) with optional translation between languages. For example, it can caption a conversation and also translate from Spanish to English on the fly, combining accessibility and multilingual communication.
• Customizable AI Assistant: A conversational assistant that users can ask for specific help (e.g., “Help me navigate to the exit,” “What does this document say in simpler words?”). This assistant adapts to the user’s needs, possibly with profiles for different accessibility preferences.

Target Users: Primarily people with disabilities – deaf or hard-of-hearing individuals, blind or low-vision individuals, and people with speech or language disorders. However, the toolkit also benefits the general public in situations where accessible communication is needed (e.g., a hearing person trying to converse with a deaf person who signs). Educators and institutions can use it to better include students with special needs. Even tourists in foreign countries could use aspects of it (like real-time translation) for communication, showing the toolkit’s broad utility.

Potential Impact: This project has profound social impact by opening up new channels of communication. For example, a real-time sign language translator using AI can enable a deaf person and a hearing person who don’t know each other’s languages to have a fluid conversation . This promotes independence and inclusion, as deaf individuals wouldn’t always need a human interpreter present. The toolkit exemplifies “AI for good,” showing how advanced tech can create “a new AI-powered paradigm for accessibility and inclusion” . In daily life, these tools could empower millions (the Lenovo example highlights 2.3 million deaf people in Brazil alone ) – helping them in education, employment, medical appointments, or social interactions. Additionally, widespread use of such AI accessibility tools could raise awareness and understanding between disabled and non-disabled communities. From an innovation standpoint, this project pushes AI beyond convenience into the realm of human rights and equality, potentially influencing how future software and devices are designed (with universal accessibility in mind).

[Sahil-Shadwal]

/assign

[github-actions]

Hey @Sahil-Shadwal! You're now assigned to this issue. Please finish your PR within 1 day.

[Sahil-Shadwal]

hey 👋 @A1L13N

Thanks for assigning me to this issue. I've gone through the codebase and wanted to share my approach before diving in – this is a pretty meaty feature and I want to make sure we're aligned.

Quick Context

The 1-day deadline caught me off guard since this involves integrating ML models, WebSockets, and accessibility features that need careful testing. I'd really appreciate 7-10 days to do this properly, but I'm flexible if you need something faster with a smaller scope.

My Game Plan

I've broken this down into manageable pieces. Here's how I'm thinking of tackling each feature:

1. Live Captioning & Translation (Starting here )

• Hook into the existing Django Channels setup you already have
• Use the SpeechRecognition library with Google's Web Speech API
• Add a WebSocket consumer that processes audio streams in real-time
• Display captions as an overlay on session pages
• Bonus: throw in language translation using Google Translate API

2. Visual Describer (For blind/low-vision users)

• Capture images from user's phone camera
• Send to a vision model (thinking OpenAI's GPT-4 Vision or similar)
• Return descriptions like "A classroom with 20 students, teacher at whiteboard"
• Also extract and read any text in images (OCR for reading signs, menus, etc.)

3. Sign Language Recognition (This one's tricky)

• Use MediaPipe Hands to track hand movements from webcam
• Feed landmarks into a pre-trained model for ASL/BSL recognition
• Output translated text in real-time
• Fair warning: this needs the most work and testing for accuracy

4. Text-to-Sign Avatar (Creative challenge)

• Build an animated character that can perform sign language gestures
• Map text input to sign language animations
• Probably start with a library of common signs and build from there

5. AI Assistant (Tying it all together)

• A conversational helper that users can ask for specific accessibility help
• "Read this page to me", "What's in this image?", "Enable captions", etc.
• Basically a control center for all the accessibility features

What I'm Adding to the Codebase

• New models: AccessibilityPreference, SignLanguageSession, CaptionSession, VisualDescription
• ML service layer for model inference
• WebSocket consumers for real-time processing
• Frontend JS for camera/mic access and UI controls
• Full test coverage (following your existing test patterns)
• Dependencies: opencv-python, mediapipe, tensorflow, speechrecognition

I'm planning to build this in phases(different PR for different phases) so you can review as I go, rather than dumping everything in one massive PR. That cool with you?

Would love to get your thoughts on:

• Any preferences on which ML models/APIs to use?
• Do you have infrastructure for running ML models (GPU servers), or should I optimize for CPU-only?