This document discusses the development of a sign language recognition system using computer vision and machine learning techniques. It begins with background on the need for such a system to help deaf individuals communicate using technology. The system works by detecting hand signs with a camera and identifying them using a convolutional neural network model. It follows a waterfall development approach with requirements including a laptop, Python software, and sufficient lighting. Benefits are helping learn sign language, while limitations include needing good lighting conditions. Future work could add subtitles to make the system more useful for media applications.

1. SIGN LANGUAGE
RECOGNITION SYSTEM

2. “
Sign language is the noblest gift god
has given to deaf people
- George Veditz -
2
Introduction

3. Background/
Problem Statement 1 3
Introduction
Problem
Statement

4. Background/ Problem Statement
◉ The desire for a computer-based solution is significant in this age of
technology for deaf people. However, researchers have been working on the
problem for quite some time, and the results are promising. Although interesting
technologies for voice recognition are becoming available, there is currently no
commercial solution for sign recognition on the market.
◉ The gesture is a vital and meaningful mode of communication for the deaf
person. So here is the computer-based method for regular people to understand
what the differently-abled individual is trying to say.
◉ In our system, the user will perform the hand gestures or signs by turning on
their camera, and the system will detect the sign and display it to the user
4
Introduction
Problem
Statement

5. WORKING OF THE PROJECT
5
Introduction
Problem
Statement
Working
of
Project

6. WORKING OF THE PROJECT
◉ In this sign language recognition system, a sign detector detects numbers, which
can be easily extended to cover a wide range of other signs and hand signs, including
the alphabet.
◉ In this system, we are using a machine learning model known as CNN.
Convolutional Neural Networks (CNNs) can learn complicated objects and patterns
because they have an input layer, an output layer, numerous hidden layers, and
millions of parameters.
◉ By turning on the camera, the user can perform the hand signs, and the system
will detect the sign and display it to the user. Using hand signs, the individual may
send out more information in a shorter amount of time.
6
Introduction
Problem
Statement
Working
of
Project

7. PROJECT LIFE CYCLE
The waterfall model is a
classical model used in the
system development life
cycle to create a system
with a linear and
sequential approach. It is
termed a waterfall
because the model
develops systematically
from one phase to another
in a downward fashion.
The waterfall approach
does not define the
process to go back to the
previous phase to handle
changes in requirements.
The waterfall approach is
the earliest approach that
was used for software
development.
7
Problem
Statement
Working
of
Project
Project
Life
Cycle

8. SYSTEM REQUIREMENT
HARDWARE REQUIREMENT
Laptop or PC
 Windows 7 or higher
 I3 processor system or
higher
 4 GB RAM or higher
 100 GB ROM or higher
SOFTWARE REQUIREMENT
Laptop or PC
 Python
 PyCharm
8
Working
of
Project
Project
Life
Cycle
System
Requirement

9. ADVANTAGE
 Helps in learning hand signs.
 CNN is very satisfactory at
picking up on design in the
input image, such as lines,
gradients, circles, or even
eyes and faces.
DISADVANTAGE/LIMITATION
 There should be sufficient
light around while detecting.
9
Project
Life
Cycle
System
Requirement
Advantages
and
Disadvantage

10. FUTURE PROSPECT
Sign Language Recognition is a breakthrough for helping deaf-mute
people and has been researched for many years. Unfortunately, every
research has its own limitations and are still unable to be used
commercially.
Therefore, We can add subtitles to it and can be used in news channels
and similar shows.
10
System
Requirement
Advantages
and
Disadvantage
Future
Prospect

11. THANKS!
Any questions?
11

12. REFERENCE
◉ https://www.researchgate.net/publication/351348553_Indian_Sign
_Language_Recognition_Using_Python
◉ https://www.irjmets.com/uploadedfiles/paper/issue_2_february_20
22/19203/final/fin_irjmets1645622414.pdf
◉ https://data-flair.training/blogs/sign-language-recognition-python-
ml-opencv/
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