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SKU: AI-4

AI Real-Time Sign Language Gesture Recognition System

Real-time hand gesture and sign language recognition system converting American/Pakistani Sign Language into text and synthetic speech using MediaPipe and LSTM.

Quick Summary & Specs

AI Real-Time Sign Language Gesture Recognition System is a complete, ready-to-deploy software & FYP codebase designed for university final year projects, academic research, and full-stack development. Includes complete source code, database schema, setup documentation, and technical support across Pakistan.

24-48h Dispatch Cash on Delivery 7-Day Support & Warranty
Rs 32000.00

500 In Stock
Tested & Quality Guaranteed 100% Working Computer Vision Pipeline. Instant Download.
Fast Local & Nationwide Shipping Same-Day Dispatch in Lahore | 2-3 Days COD to Karachi, Islamabad & All Pakistan.

AI Real-Time Sign Language Translation System (Audio/Text Output)

An assistive AI engineering project that bridges the communication gap for deaf and mute individuals. Using MediaPipe to extract 21 3D hand landmarks in real time and a Temporal Long Short-Term Memory (LSTM) Recurrent Neural Network, the system translates dynamic sign gestures into English/Urdu text and natural spoken voice output.

🧠 Architecture & Tech Stack:

- Landmark Extraction: Google MediaPipe Hand Holistic Tracker (63 spatial coordinates per frame)
- Sequence Classifier: Multi-layer Bidirectional LSTM in TensorFlow / Keras
- Voice Synthesis: pyttsx3 / gTTS Text-to-Speech Engine
- Interface: Clean, low-latency Tkinter GUI / Streamlit web dashboard

📦 Complete Deliverables:

- Full Python Source Code with real-time video inference loop
- Custom dataset collector script & pre-trained LSTM gesture model
- Complete Final Year Project Documentation & Defense Slides

What's Included in the Package:

  • Full Python Source Code & Real-Time Inference Scripts
  • Pre-trained LSTM Sign Language Model Weights
  • Custom Hand Landmark Gesture Training Dataset
  • Interactive User Interface with Text-to-Speech Output
  • Complete Final Year Project (FYP) Thesis & Report
  • Viva Presentation Slides & Flowchart Architecture
  • Quick-Start Setup Guide & Remote Support

Software Setup & Deployment Guide:

Step 1: Extract Source Code & Docs

Download and extract the complete source code package, database schema, and architecture documentation provided with your order.

Step 2: Install Project Dependencies

Open the project directory in VS Code or your preferred IDE and install the required runtime libraries and packages listed in the setup guide.

Step 3: Configure Environment & DB

Set up your environment configuration variables and run the included database migrations or seed scripts.

Step 4: Launch & Verify Modules

Start the local development server or build executable and test all user, admin, and core functional workflows.

Software FYP Note: Includes 100% unencrypted source code, database files, deployment documentation, and 7-day setup assistance across Pakistan.
Custom Prototyping & FYP Enclosures

Need a Custom 3D Printed Casing or Multilayer PCB?

Get custom snap-fit enclosures and robotics parts printed via our custom 3D printing service in Lahore & Pakistan, or order industrial double-sided circuit boards through our custom PCB fabrication service with nationwide delivery.

Key Features

Image-Based Detection It Users can upload an image to identify specific signs.
Inclusive Communication Aims to bridge the communication gap between sign language users and non-signers.
Learning Mode Interactive feature that helps users learn and practice different signs with instant feedback.
Real-Time Recognition Integrates with DroidCam to capture live video and detect signs in real-time.
Sign Detection with MediaPipe It Uses MediaPipe to detect and track hand landmarks for accurate sign recognition.

Specifications

Backend Django (Python)
Database SQL LITE
Deployment Dockerized container (manual installation requires extra setup cost of 5000)
Frontend HTML, CSS, JavaScript, Bootstrap
Library MediaPipe for hand/sign detection
Note If You Need AnyKind of Code Snippet Related To This Project We Will Provide
Ram/Processor 8gb/3.5Ghz

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