sign language detection and text or speech conversion
1. SIGN LANGUAGE CONVERSION
Supervised By:-
PROF. A.K. NANDA
(Assistant professor)
Presented By :-
Name :- LITANSHU SAHOO
Registration no. :- 2201110037
GOVERNMENT COLLEGE OF ENGINEERING, KALAHANDI-766002
3. INTRODUCTION
What is sign language conversion?
Sign language conversion refers to the process of translating between
sign languages and spoken or written languages, often using technology
like computer vision and machine learning to recognize hand gestures and
convert them into text or speech, and vice versa.
4. MOTIVATION
Bridging Communication Gaps and Enhancing Accessibility
•For Individuals with Hearing Impairments:
•Sign language conversion technologies empower individuals who are deaf or hard-of-
hearing to communicate more effectively with those who don't know sign language,
fostering independence and participation in various aspects of life.
•Robotics and Virtual Reality:
•Sign language recognition and conversion can also be applied in robotics and virtual
reality, enabling more natural and intuitive interactions.
•In Education and Healthcare:
•Sign language conversion can be crucial in educational settings and healthcare
environments, ensuring that deaf and hard-of-hearing individuals can access information
and participate fully.
•Eliminating the Need for Interpreters:
•Sign language conversion systems can reduce the reliance on human interpreters,
providing greater independence and privacy for individuals with hearing impairments.
5. METHODOLOGY
1. Data Acquisition & Preprocessing:
•Cameras and Sensors: Cameras and sensors capture the movements and
expressions of the signer.
•Image/Video Capture: Sign language gestures are captured as images or
videos.
•Data Segmentation: The captured data is segmented to isolate the signer's
hands and body.
•Feature Extraction: Important features like hand shape, palm orientation,
location, movement, and non-manual signals are extracted from the segmented
data.
6. METHODOLOGY
Recognition & Classification:
•Computer Vision:
•Computer vision techniques are used to analyze the video or sensor data
captured from the signer.
•Gesture Recognition:
•Algorithms are used to recognize and classify different sign language gestures.
•Machine Learning Models:
•Machine learning models, such as deep learning models, are trained to
recognize sign language gestures.
•Convolutional Neural Networks (CNNs):
•CNNs are used to recognize and extract characteristics from sign language
motions.
•Recurrent Neural Networks (RNNs):
•RNNs are used to process the sequence of data points and predict the sign
language gesture being performed.
9. APPLICATION
•Signly: A mobile app for sign language translation.
•Automatic Translate Real-Time Voice to Sign Language Conversion: A
system for converting voice to sign language for deaf and dumb people.
•Sign Language Translator Application (IJERT): An application using machine
learning and image processing to recognize signs from input images.
•Sign Language Translator Application (ResearchGate): A trilingual mobile
app for translating speech into international sign language, converting audio
information into text and sign language.
•Indian Sign Language Converter System (ResearchGate): An Android app for
Indian sign language conversion.
10. ADVANTAGES
For Deaf and Hard-of-Hearing Individuals:
•Facilitates Communication:
•Sign language conversion technologies and interpreting services bridge the
communication gap, enabling deaf and hard-of-hearing individuals to participate
fully in conversations and access information.
•Promotes Inclusivity:
•By making communication accessible, sign language conversion fosters a more
inclusive society where everyone can participate and share ideas.
•Access to Information:
•Sign language conversion allows deaf and hard-of-hearing individuals to access
information presented in spoken language, such as news, lectures, and
entertainment.
•Early Language Development:
•For children with hearing loss, early exposure to sign language can positively
impact their language development and speech production skills.
11. ADVANTAGES
For Hearing Individuals:
•Enhanced Communication Skills:
•Learning sign language improves overall communication skills and fosters
empathy and understanding for the Deaf community.
•Cognitive Benefits:
•Sign language can enhance cognitive functions like memory, attention, and
problem-solving.
•Cultural Awareness:
•Learning sign language can increase cultural awareness and understanding of
Deaf culture.
•Career Opportunities:
•Sign language interpreters and translators are in demand, offering career
opportunities in various fields.
•Improved Communication in Diverse Situations:
•Sign language can be used in situations where spoken language is not possible,
such as in noisy environments or underwater.
12. DISADVANTAGES
1. Inconsistency and Accuracy:
•Multiple Translators:
•Relying on multiple interpreters can lead to inconsistent translations, as different
individuals may interpret signs and nuances differently.
•Costly Mistakes:
•Translators, even skilled ones, can make mistakes, potentially altering the intended
message or causing misunderstandings.
•Lack of Uniformity:
•The lack of standardized sign language dictionaries and grammar rules can lead to
inconsistencies in translations, especially when dealing with multiple languages.
13. DISADVANTAGES
Data and Technological Limitations:
•Limited Data Availability:
•Compared to other forms of language data, sign language data is limited, making it
difficult to train reliable AI systems for sign language recognition and translation.
•Complexities in Sign Language:
•Sign language is complex, relying on the position, shape, and movement of hands,
face, and body, which can be difficult for AI to capture accurately.
•Technological Limitations:
•Computer vision technology used to detect and interpret hand gestures and
movements can be challenging to make accurate and real-time.
•Hardware Limitations:
•Wearable devices for sign language translation can have limitations in size, weight,
and processing power, affecting their usability and effectiveness.
•Real-time Processing:
•Real-time processing is crucial for smooth and uninterrupted communication,
which demands better algorithms and hardware.
14. CONCLUSION
Sign language conversion technology, while promising, faces
challenges but holds immense potential for bridging communication
gaps, particularly for the deaf and hard-of-hearing, and requires ongoing
refinement and development.
15. FUTURE SCOPE
Enhanced Accuracy and Real-time Translation:
•Advanced Algorithms:
•Future advancements will refine algorithms and machine learning methods to
improve the accuracy of sign language recognition (SLR).
•Deep Learning Techniques:
•Deep learning techniques, such as convolutional and recurrent neural networks,
are expected to continue improving the accuracy and speed of identifying sign
gestures.
•Real-time Conversion:
•Real-time systems converting sign language to text and speech will be
developed, integrating deep learning techniques.
16. REFERENCES
•Sign Languages by Cambridge University Press: This book contains information on sign
languages from pages 618–669.
•Aspects of the Syntax of American Sign Language by D. Aarons: A PhD dissertation from
Boston University.
•Lexical tense markers in American Sign Language by D. Aarons, B. Bahan, J. Kegl, and
C. Neidle: In Language, Gesture and Space.
•Pronominal system in Croatian Sign Language by T. Alibašić Ciciliani and R. B.
Wilbur: In Sign Language and Linguistics.