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🖐 Real-Time Sign Language Recognition using Transfer Learning

Academic Major Project (B.Tech – Computer Science & Engineering)
Institution: Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad
Year: 2023
Mentor: Dr. Lipika Goel, Associate Professor
Team Members:

  • Pavan Karthik (19241A05L6)
  • Pradyumna Sinha (19241A05M0)
  • Jashwanth Naidu (19241A05J4)
  • Anand Thota (19241A05M6)

📄 Overview

This repository contains the final major project report completed as part of the Bachelor of Technology in Computer Science and Engineering.
The project focused on developing a real-time sign language recognition system using transfer learning and the TensorFlow Object Detection API.

The goal was to bridge the communication gap between hearing-impaired and normal individuals by recognizing hand gestures from a webcam feed and translating them into corresponding text in real time.

🧠 Abstract

Communication is essential for human interaction, but the hearing-impaired community often faces barriers due to lack of common language understanding.
This project proposes a solution that captures hand gestures via webcam and recognizes them using a deep learning model fine-tuned through Transfer Learning.
By leveraging a pretrained SSD MobileNet V2 model, the system accurately identifies common sign gestures and displays their meanings instantly.

⚙️ Technologies Used

Tool / Library Purpose
Python 3 Core programming language
OpenCV Capturing and processing video frames
TensorFlow 2.x Model training and detection
NumPy Array and matrix computations
LabelImg Manual image labeling
Jupyter Notebook Model development and testing

🧩 System Workflow

  1. Capture sign language gesture images using a webcam
  2. Label gestures using LabelImg
  3. Split into training and test datasets
  4. Convert to TFRecords and create label maps
  5. Fine-tune SSD MobileNet V2 (COCO) using Transfer Learning
  6. Perform real-time detection on webcam input

🎯 Project Scope

  • Detects six basic sign language gestures: Hi, Yes, No, Thank You, Good, I Am Fine
  • Demonstrates the effectiveness of Transfer Learning with limited datasets
  • Enables a low-cost and accessible method for real-time communication

🧾 Citation

Pavan Karthik, Pradyumna Sinha, Jashwanth Naidu, Anand Thota.
"Real-Time Sign Language Recognition Using Transfer Learning."
Major Project Report, GRIET, Hyderabad, 2023.

🏷️ License

This repository is shared for academic and portfolio purposes only.
All rights are reserved by the original authors and Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad.