click here to download project abstract of sign language

At data pro , we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.

ABSTRACT

The COVID-19 pandemic ushered in a seismic shift in the way we live and communicate, thrusting us abruptly from traditional offline modes to a digital realm dominated by the internet.

This disparity prompted the development of a groundbreaking solution: a Convolutional Neural Network (CNN) based model tailored for Indian sign language recognition.

The essence of our approach is rooted in a thoughtfully assembled dataset, painstakingly curated through preprocessing methods, notably employing techniques like label binarization. Hence feature extraction is a critical step, involving the delineation of palm and finger regions through distinct models.

The dataset, enriched with these features, overall becomes the training ground for a custom-built CNN model. This neural network, then with specified learning parameters including a 0.001 learning rate, a batch size of 128, and 10 epochs, achieved remarkable results.

In rigorous testing, the model demonstrated a 93% accuracy rate in recognizing hand gestures. This exceptional performance underscores the viability and effectiveness of our proposed solution.

The potential impact of this innovation extends beyond the confines of our study. Then by integrating our model into various online meeting platforms, we envisage a transformative experience for the target audience, fostering inclusivity and breaking down communication barriers.

In a world reshaped by the pandemic, our CNN-based sign language recognition model emerges as a beacon of technological ingenuity, heralding a more accessible and inclusive digital future.

Indian Sign Language for Deaf and Dumb

Abstract:

This project focuses on developing an advanced platform for learning and communicating in Indian Sign Language (ISL) to bridge the communication gap for the deaf and mute community. Leveraging Python and web technologies, the proposed system incorporates computer vision and machine learning techniques to recognize and translate ISL gestures. The web-based interface enhances accessibility, providing an interactive and inclusive platform for learning and communication.

Existing System:

Current systems for learning and communication in sign language often rely on static images, videos, or textual representations. These approaches may lack real-time interactivity, personalized learning, and the ability to facilitate dynamic conversations in sign language.

Proposed System:

The proposed system introduces a comprehensive platform for learning and communicating in Indian Sign Language. Utilizing computer vision and machine learning, the system allows users to interact with the platform in real-time, translating their signed gestures into text or speech. The web interface enhances accessibility, enabling users to learn, practice, and communicate in ISL through an intuitive and interactive online environment.

Modules Explanation:

1. Gesture Recognition Module:

• Utilizes computer vision techniques to recognize and interpret ISL gestures in real-time.

1. Translation Module:

• Translates recognized gestures into text or speech for improved communication.

1. Learning Module:

• Provides interactive lessons and exercises for users to learn and practice ISL.

1. Communication Interface:

• Enables users to communicate with each other using ISL gestures, with real-time translation.

1. Web User Interface:

• Offers a user-friendly platform for learning, practicing, and communicating in ISL.

System Requirements:

• Hardware:
• Standard computer or mobile device with a camera for gesture recognition.
• Microphone for speech recognition (optional).
• Software:
• Python for implementing computer vision and machine learning algorithms.
• Web development framework (e.g., Flask or Django).

Algorithms:

• Computer Vision Algorithms:
• Utilizes image processing and gesture recognition algorithms for interpreting ISL gestures.
• Machine Learning (Optional):
• Trains models for personalized gesture recognition and translation.

Hardware and Software Requirements:

• Hardware:
• Standard computer or mobile device with a camera.
• Microphone (optional).
• Software:
• Python 3.x
• Web development framework (Flask or Django).

Architecture:

• Gesture Recognition:
• Uses computer vision techniques to interpret ISL gestures in real-time.
• Translation:
• Translates recognized gestures into text or speech.
• Learning Platform:
• Provides interactive lessons and exercises for users to learn and practice ISL.
• Communication Interface:
• Facilitates real-time communication using ISL gestures, with translation features.
• Web User Interface:
• User-friendly interface for learning, practicing, and communicating in ISL.

Technologies Used:

• Python, computer vision libraries for gesture recognition.
• Web development frameworks (Flask/Django) for creating the web interface.
• Machine learning libraries (e.g., Scikit-learn) for optional personalized gesture recognition.

Web User Interface:

The web interface offers a comprehensive and interactive learning platform. Users can access lessons, practice ISL gestures, and communicate with others using sign language. Real-time translation features ensure effective communication, and the user-friendly design makes the platform accessible to individuals with varying levels of proficiency in ISL. The interactive web interface promotes inclusivity and empowers the deaf and mute community in their communication endeavors.

UML DIAGRAMS

Collaboration Diagram

Architecture diagram

class diagram

sequence diagram

use case diagram

activity diagram

component diagram

Deployment Diagram

Flow chart Diagram