Sign To Speech Conversion

Abstract

1. Introduction

                               [](https://github.com/beingaryan/Sign-To-Speech-Conversion/issues) [](https://github.com/beingaryan/Sign-To-Speech-Conversion/network/members) [](https://github.com/beingaryan/Sign-To-Speech-Conversion/stargazers) [](https://github.com/beingaryan/Sign-To-Speech-Conversion/issues) [](https://www.linkedin.com/in/aryan-gupta-6a9201191/)

A language translator is extensively utilized by the mute people for converting and giving shape to their thoughts. A system is in urgent need of recognizing and translating sign language. Lack of efficient gesture detection system designed specifically for the differently abled, motivates us as a team to do something great in this field. The proposed work aims at converting such sign gestures into speech that can be understood by normal people. The entire model pipeline is developed by CNN architecture for the classification of 26 alphabets and one extra alphabet for null character. The proposed work has achieved an efficiency of 99.88% .

Sign Language to Speech Conversion system built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to communicate using American Sign Language(ASL) based gestures in real-time video streams with differently abled.

2. Objective

Sign Language Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras frameworks.

This project demonstrates how Jupyter Notebook, Deep Learning, Keras can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Gaussian filter is used as a pre-processing technique to make the image smooth and eliminate all the irrelevat noise.
• Intensity is analyzed and Non-Maximum suppression is implemented to remove false edges.
• For a better pre-processed image data, double thresholding is implemented to consider only the strong edges in the images.
• All the weak edges are finally removed and only the strong edges are consdered for the further phases.

4. Technology Stack

• TensorFlow

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/beingaryan/Sign-To-Speech-Conversion.git
cd Sign-To-Speech-Conversion

1. Start and fork the repository.
2. Clone the repo
3. Change your directory to the cloned repo and create a Python virtual environment named 'test'
4. Now, run the following command in your Terminal/Command Prompt to install the libraries required

$ git clone https://github.com/beingaryan/Sign-To-Speech-Conversion.git

$ mkvirtualenv test

$ pip3 install -r requirements.txt

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by beingaryan and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.