Sign Language Recognition using CNN

A deep learning-based computer vision project that recognizes hand gestures from sign language using a Convolutional Neural Network (CNN). This system aims to bridge the communication gap between hearing/speech-impaired individuals and others by translating gestures into meaningful outputs.

Project Overview

Sign language is an essential mode of communication for people with hearing and speech impairments. This project leverages deep learning and image processing techniques to build a model capable of identifying hand gestures and classifying them into corresponding sign language labels. The model is trained on image data and can be extended to work in real-time applications.

Key Features

• Image-based hand gesture recognition
• CNN model for automatic feature extraction
• Data preprocessing and augmentation
• Model training, validation, and evaluation
• Scalable for real-time prediction using webcam

🛠️ Tech Stack

Programming Language

• Python

Libraries & Frameworks

• TensorFlow / Keras
• OpenCV
• NumPy
• Matplotlib

Concepts Used

• Convolutional Neural Networks (CNN)
• Image Processing
• Deep Learning

📂 Project Structure

Sign_Language_Recognition/
│
├── dataset/                # Image dataset for training and testing
├── core/                   # Core modules (model, preprocessing, etc.)
├── regression_model.py     # Model implementation (CNN logic)
├── requirements.txt        # Required dependencies
├── README.md               # Project documentation

⚙️ How It Works

1. Data Collection

• Images of hand gestures are collected and organized into labeled categories.

2. Data Preprocessing

• Images are resized, normalized, and cleaned using OpenCV. *Data augmentation is applied to improve model performance.

3. Model Building

• A CNN model is designed with:
• Convolution layers (feature extraction)
• Pooling layers (dimensionality reduction)
• Fully connected layers (classification)

4. Training & Evaluation

• The model is trained on the dataset and validated to check accuracy and loss.

5. Prediction

• The trained model predicts the gesture class from new input images.

📊 Results

• Achieved high accuracy in classifying hand gestures
• Model performance improved through hyperparameter tuning and preprocessing
• Successfully demonstrates the capability of CNNs in image classification tasks

(You can add exact accuracy here if available, e.g., 92% accuracy)

▶️ Installation & Setup

1. Clone the repository

git clone https://github.com/Varsh-raj/Sign_Language_Recognition.git
cd Sign_Language_Recognition

2. Install dependencies

pip install -r requirements.txt

3. Run the project

python regression_model.py

📌 Future Enhancements

• Real-time gesture recognition using webcam
• Deployment as a web application
• Support for full sentence translation

⭐ Acknowledgements

• Open-source libraries like TensorFlow and OpenCV
• Online resources and research papers on CNN and image recognition