1 INTRODUCTION
Sign Language
is the visual manner to convey the message for Deaf and Dumb Peoples. It is a combination
of gestures, orientations, movements of hands, arms or body and facial
expression[1]. Like a normal language, Sign Language is also varying
considering different factors. Various standard sign languages available in the
real world are Indian Sign Language (ISL), American Sign Language (ASL) etc.
According to a great Author Paul J. Meyer, Communication – the human connection
is the key to personal and career success [2]. Communication is the important
term in real world to allow others and ourself to understand information more
accurately and quickly. But since everyone else cannot understand this Sign
Language, communication between the Deaf and the Dumb is difficult. This
project provides one of the solutions to increase the communication of Deaf and
Dumb peoples with the normal peoples. In this digital era, Mobile application
is the best solution for everyone to use, so by using the capabilities of
Machine Learning and Image processing algorithms available in Tensor Flow
Library we make the working mobile application[3]. In which, user has to
capture image as input and got the output in terms of text and audio. This will
ease the medium for those special peoples and in some cases using the Frequent
Phrases feature of our application, there is no need of capturing the photos
just they need to press singlebutton[4].
2. BACKGROUND
In
the past, communication between deaf and hearing individuals was often limited
to written notes or gestures. However, with the advent of new technology, such
as the Sign Language Translator, communication has become more accessible and
inclusive for the deaf community. The Sign Language Translator uses advanced
artificial intelligence and computer vision algorithms to recognize and
interpret signs and gestures[5].
3. METHODOLOGY
To develop the Sign Language
Translator, researchers used machine learning algorithms to train the system to
recognize and translate signs and gestures from multiple sign languages,
including American Sign Language (ASL) and British Sign Language (BSL). The
system consists of a camera or sensor that captures the signer's movements and
converts them into data. The machine learning algorithms then process this data
to identify the signs and gestures that are utilised in the language. The system
can translate signs and gestures into, text.
Block Diagram:
Fig. 1- Block Diagram
4. PROJECT
OVERVIEW
The initiative's
primary goal is to improve the deaf community's ability to engage and
communicate with others around them. The intention is to transform the 26 basic
characters that make up the English alphabet. script them, then show them on a
smartphone.
Fig. 2- The
fingers, thumb, and palm bends are detected
The
concept of using hand motions to control a robotic arm served as the
inspiration for the project[6]. The majority of the job is consistent, but
putting the other portions into practise is a challenging undertaking. An
accelerometer measures the tilt of the palm. Four bend sensors are located on
the fingers of a glove, and one is located on the thumb. The fingers, thumb,
and palm bends are detected by these sensors. The bend angle value is used by
the Arduino Nano microcontroller to identify the set of values that belong to
each symbol. The Arduino Nano then sends the appropriate result value over
Bluetooth to the Android app, which displays the generated symbol.
While
working on this project, there is a significant problem that arises every time
we put on the glove: it requires constant calibration. Additionally, when a
person is changing, it is important to calibrate and check based on their hand
and gesture.
The
accuracy was increased by routinely refreshing the data set for each sign.
But
for now, we've created a few movements for a prototype to demonstrate that the
project is operational, and with more study, we can address the aforementioned
problems.
5. RESULTS
The
Sign Language Translator has many potential applications, including in
education, healthcare, and business settings. In education, the system can help
teachers communicate with deaf students and make their lessons more accessible.
In healthcare, the system can help medical professionals communicate with deaf
patients and provide them with better care. In business settings, the system
can help deaf individuals communicate with hearing colleagues and clients.
6. BENEFITS
The
Sign Language Translator can improve communication between deaf and hearing
individuals, making the world more inclusive for the deaf community. The
technology can also help break down language barriers and promote equal access
to education, healthcare, and employment opportunities.
7. CHALLENGES
The
Sign Language Translator technology is still in its early stages and faces
several challenges. One of the major challenges is the variability of sign
languages across regions and cultures. Different sign languages have different
signs and grammar rules, making it difficult to develop a universal system that
can recognize and translate all sign languages accurately.
8. CONCLUSION
The
Sign Language Translator is a promising technology that has the potential to
improve the lives of deaf individuals by making communication more accessible.
While there are challenges to overcome, the continued development and
refinement of this technology can lead to a more inclusive and accessible world
for all.