# ASL Recognition using Convolutional Neural Networks
This project aims to recognize American Sign Language (ASL) gestures using Convolutional Neural Networks (CNNs). The code provided trains a CNN model on an ASL dataset and evaluates its performance.
## Dataset
The ASL dataset used in this project contains images of ASL gestures representing letters and numbers. The dataset consists of 36 classes, including digits 0-9 and letters A-Z.
The dataset is organized in a directory structure where each class has its own subdirectory containing the corresponding images.
### Prerequisites
To run the code, you need the following dependencies:
- Python (3.6 or higher)
- TensorFlow (2.0 or higher)
- Matplotlib
### Installation
1. Clone the repository:
```shell
git clone https://github.com/your_username/your_repository.git
-
Install the required dependencies:
pip install tensorflow matplotlib
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Download the ASL dataset and place it in the
asl_dataset
directory. -
Open the code in your preferred Python development environment.
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Run the script to train the ASL recognition model:
python main.py
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The script will load the dataset, preprocess the images, define the CNN model, train it on the training set, and evaluate its performance on the test set.
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Once the training is complete, the model's performance metrics, such as loss and accuracy, will be displayed.
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You can modify the code to experiment with different hyperparameters, network architectures, or data augmentation techniques.
The trained model achieves an accuracy of X% on the test set. You can further analyze the performance and make improvements based on your specific requirements.
- The ASL dataset used in this project was obtained from
- https://www.kaggle.com/datasets/grassknoted/asl-alphabet