Gesture-Recognition-Using-Sign-Language

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American Sign Language (ASL) Gesture Recognition

This project uses deep learning (CNN) to recognize static hand gestures representing the American Sign Language alphabet (A-Y, excluding J).

Dataset

• sign_mnist_train.csv: Training images (28x28 grayscale).
• sign_mnist_test.csv: Testing images.
• csv file

Tools & Libraries

• Python, TensorFlow/Keras
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn

Project Structure

├── Gesture_Recognition_Using_Sign_Language.ipynb
├── sign_mnist_train.csv
├── sign_mnist_test.csv
├── images/
└── README.md

How to Run

1. Open the Jupyter notebook.
2. Run all cells to preprocess data, train the model, and evaluate.
3. View training/validation graphs, confusion matrix, and predictions.

Key Features

• CNN-based image classification
• Accuracy and loss visualization
• Confusion matrix and class-wise metrics
• Sample misclassification analysis

Results (Example)

Metric	Value
Training Accuracy	98.5%
Validation Accuracy	97.2%
Test Accuracy	96.7%

Notes

• Letters 'J' and 'Z' are excluded as they involve motion.
• All input images are normalized 28x28 grayscale.