Sign Language Recognition (ISL & ASL)

This project focuses on developing a machine learning model to recognize static gestures in Indian Sign Language (ISL) and American Sign Language (ASL). The goal is to classify images of hand gestures into corresponding symbols (letters or words) to aid communication for the hearing and speech-impaired.

Project Overview

• Dataset: Uses the ISL and ASL datasets from Kaggle, containing labeled images of hand gestures.
• Model: A Convolutional Neural Network (CNN) is used to classify the images, with transfer learning to leverage pre-trained models like ResNet50 or MobileNetV2.
• Preprocessing: Images are resized to a standard resolution, and data augmentation techniques are applied to improve the model’s performance and robustness.

Features

• Combines ISL and ASL datasets for broader recognition.
• Visualizes class distribution using bar plots.
• Implements a CNN-based classification model.
• Trains the model using Google Colab for GPU support.

Requirements

• Python
• TensorFlow
• Keras
• Matplotlib
• Seaborn
• Pillow

References

1. Indian Sign Language (ISL) Dataset: Kaggle ISL Dataset
2. American Sign Language (ASL) Dataset: Kaggle ASL Dataset
3. TensorFlow Documentation: TensorFlow Image Classification

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