DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

AND
INFORMATION TECHNOLOGY

MINI PROJECT PRESENTATION

SECTION : IT-A
BATCH : : 2
Name Roll No.
1. AKITI AKSHAYA 21BK1A120
4
2. KUNTA AKHIL 21BK1A126
3
PROJECT GUIDE HOD CSE – IT
3. AKULA RAJ ROHAN 21BK1A120
5 Mr. D. Harith Reddy
Gestures of India: An Insight
into Indian Sign Language
PROBLEM STATEMENT
The hearing-impaired community in India faces significant communication barriers due to the
lack of effective tools for interpreting Indian Sign Language (ISL) in real-time. Existing systems
struggle with inability to handle dialect variations, limited large-scale datasets, difficulty in
recognizing double-handed gestures and continuous sign sequences, and issues with background
clutter and environmental factors. This communication gap hinders access to essential services,
education, and day-to-day interactions, creating a need for an advanced solution to improve
accessibility for the deaf and hard-of-hearing community in India.
PROPOSED SOLUTION
In our proposed system, the main aim is to develop an optimal and accurate and real-time Indian
Sign Language (ISL) detection system that can effectively bridge the communication gap
between the hearing and deaf communities. Our system should be capable of recognizing a wide
range of ISL signs, including double handed signs, we use CNN and various deep learning
models for recognizing continuous sign sequences, and gestures with varying hand shapes,
orientations. It should also be robust to environmental factors such as lighting conditions,
background clutter.
 EXISTING SOLUTIONS VS PROPOSED
Existing Solutions: SOLUTION
• Lack of Standardization: Due to dialect variations and the absence of standardized ISL resources,
existing systems struggle to generalize well.
• Dataset Limitations: Current systems lack large-scale ISL datasets, making it hard to train robust
models.
• Environmental Challenges: Existing solutions are not robust to environmental factors like lighting
variations and background clutter.
Proposed Solution:
• Improved Accuracy: By using CNN and deep learning algorithms, your system aims to achieve higher
accuracy, especially in recognizing double-handed signs and continuous sequences.
• Addressing Dialect and Dataset Issues: Your system will incorporate a broader range of ISL gestures,
helping overcome dialectical variations, and might benefit from more comprehensive datasets or data
augmentation techniques.
• Environmental Robustness: The proposed system will be more robust against background clutter and
varying lighting conditions, ensuring better real-world performance.
TECHNOLOGIES USED
Programming Languages:
• Python(3.11.4)
Web Based Tools:
• HTML
• CSS
Scripting Languages:
• JavaScript
Frameworks & Libraries:
• NumPy
• TensorFlow
• MediaPipe
• BASE64
• Flask
• Flask-SocketIO
• EmailJS
IDE:
• VsCode
SOFTWARE AND HARDWARE REQUIREMENTS

SOFTWARE REQUIREMENTS
Operation System: Windows (11/12)
Scripting Languages: HTML/PHP & CSS
Text editor: Notepad
Programming Languages: Python (3.11.4)
IDE: VSCode
Libraries: NumPy, CV2, Keras, TensorFlow

HARDWARE REQUIREMENTS

Devices Computer or Laptop

RAM: 8GB
Hard Disk: 16-32GB
Image capturing devices: Cameras or Webcams
Resources: Lighting & Background
UML DIAGRAMS
USE-CASE DIAGRAM
CLASS DIAGRAM
ACTIVITY DIAGRAM
SEQUENCE DIAGRAM
Software Testing Methodology

Unit Testing:
Test Input: The input to the unit test will typically be images or videos of double-hand gestures. These
are passed to the recognition system.
Preprocessing: The test should ensure that the system correctly preprocesses both hand images, such
as resizing, normalizing, and extracting relevant features (keypoints, edges) for both hands.
Feature Matching: The unit test verifies that the system can independently recognize features from
both hands, ensuring symmetry or coordinated movement patterns are correctly identified.
Prediction: The test will check if the system predicts the correct sign based on the input.
Edge Cases: The unit test should also cover edge cases like incomplete gestures, interference between
hands.
PROJECT OUTPUT
BENEFITS AND LIMITATIONS
Benefits of the Proposed System
1. Improved Communication: The system will enable real-time communication between hearing and
hearing-impaired individuals, fostering better understanding.
2. Wide Sign Recognition: Capable of recognizing both single-handed and double-handed gestures,
including continuous sign sequences, which enhances its versatility in ISL interpretation.
3. Accuracy and Efficiency: By using CNNs and deep learning models, the system aims to achieve
higher accuracy in gesture recognition, reducing errors and improving reliability.
4. Environmental Adaptability: The system is designed to function effectively even in challenging
environments with varying lighting and background clutter, making it more practical for real-world
usage.
5. Real-time Processing: Facilitates immediate translation of signs to text, ensuring seamless
communication without significant delays.
Limitations of the Proposed System
1. Data Dependency: The system relies heavily on large-scale, well-labeled ISL datasets. Limited
availability of such datasets may impact performance.
2. Dialectical Variations: Differences in regional ISL dialects may pose challenges in recognizing
certain gestures uniformly.
3. Hardware Requirements: Real-time processing of video inputs using deep learning models may
require high-performance hardware, which could limit accessibility in resource-constrained
environments.
4. Complex Gestures: Recognizing complex and overlapping gestures in continuous sequences can
still be challenging, potentially leading to misinterpretation.
5. Limited to ISL: The system focuses solely on Indian Sign Language, limiting its application for
speakers of other sign languages.
Future Enhancement

To further enhance the capabilities of our system, future developments will focus on expanding its
functionality to recognize words and sentences. Integrating natural language processing (NLP)
techniques will allow the system to understand context and grammar, enabling more
comprehensive and fluent communication. These enhancements will significantly improve the
user experience and broaden the scope of applications for our Indian Sign Language Recognition
system.
CONCLUSION
In this minor project, we successfully trained a model to recognize and interpret the alphabets of
the Indian Sign Language (ISL). This foundational work is a significant step towards developing
a comprehensive system for understanding and translating ISL. By focusing on the alphabets, we
have created a robust base that will facilitate the expansion of our project to include words and
more complex phrases. In conclusion, while this project is still in its early stages, the successful
training of ISL alphabets lays a strong foundation for future advancements.

PROJECT GUIDE
THANK YOU