anjalipathak03/sign-language-recognation-project

Sign-Language-Recognition-for-differently-abled-people (Minor Project)

Sign Language Recognition Project

Abstract

• Sign language is a visual language used by people with hearing disabilities to communicate with each other and with hearing people. However, understanding sign language can be a challenge for those who are not familiar with it. Machine learning and deep neural networks can be used to detect sign language gestures and translate them into text or speech to help bridge this communication gap.

• In this project, a machine learning model and deep neural network are developed to detect sign language gestures. The model is trained on a large dataset of sign language gestures and uses image processing techniques to detect and track the movements of the hands and fingers. The deep neural network is used to classify the detected gestures into corresponding sign language words or phrases.

• The project is evaluated on a separate testing dataset and achieves high accuracy in detecting and translating sign language gestures.

Steps

The project will involve the following key steps:

1. Data collection: Collecting a large dataset of sign language gestures, covering a wide range of signs and expressions.
2. Data pre-processing: Cleaning and preparing the dataset for training the Machine Learning and Deep Learning models.
3. Model selection: Selecting appropriate Machine Learning and Deep Learning models, such as CNNs, RNNs, or attention-based models, for sign language recognition.
4. Model training: Training the selected models on the prepared dataset to achieve high accuracy and performance.
5. Integration: Integrating the trained models with text-to-speech or speech-to-text software to enable smooth communication.
6. Testing and Evaluation: Testing the system on a variety of sign language gestures and evaluating its accuracy and performance.

Python library

1. Lastest pip -> pip install --upgrade pip

2. numpy -> pip install numpy

3. os-sys -> pip install os-sys

4. opencv -> pip install opencv-python

5. tensorFlow -> pip install tensorflow

6. keras -> pip install keras

7. tkinter -> pip install tk