The Sign Language Recognition project aims to develop a machine learning model capable of recognizing and interpreting sign language gestures. By leveraging computer vision and deep learning techniques, the project seeks to bridge communication barriers for individuals with hearing impairments by accurately translating sign language gestures into text or spoken language.
To set up the Sign Language Recognition project, follow these installation steps:
-
Prerequisites: Ensure that Python 3.7 or higher is installed. Additionally, install the following libraries using pip:
-
Clone the Repository:
-
Install Dependencies:
To use the Sign Language Recognition project, follow these steps:
-
Starting the Application:
-
User Interface: Visit http://localhost:5000 in your web browser to access the sign language recognition interface.
To train the sign language recognition model, use the following steps:
-
Data Preparation: Obtain a labeled dataset of sign language gestures and organize it into train and test sets.
-
Training Procedure: Use the provided training script to train the model:
To evaluate the trained model, follow these steps:
-
Testing Methodology: Utilize the testing script to assess the model's accuracy:
-
Sample Input: Provide sample sign language gestures along with their expected outputs for testing.
Contributions to the Sign Language Recognition project are welcome. If you would like to contribute, please follow these guidelines:
-
Bug Reports: Submit bug reports and issues through the GitHub issue tracker.
-
Feature Requests: Suggest improvements or new features by creating a pull request or starting a discussion in the issue tracker.
The Sign Language Recognition project is licensed under the MIT License. See the LICENSE file for details.
[Optional: Include additional sections such as Acknowledgments, FAQ, or Troubleshooting if relevant to your project.]