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Sign Language Abstract

The Sign Language to Text Converter is an AI-based system designed to facilitate communication between individuals with hearing and speech impairments and the hearing community by converting sign language gestures into readable text in real-time. It utilizes computer vision techniques and deep learning models to accurately recognize signs captured through a camera. This innovation aims to enhance inclusivity and accessibility, with potential future expansions to support multiple sign languages and improve translation capabilities.

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22r11a05t5
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13 views1 page

Sign Language Abstract

The Sign Language to Text Converter is an AI-based system designed to facilitate communication between individuals with hearing and speech impairments and the hearing community by converting sign language gestures into readable text in real-time. It utilizes computer vision techniques and deep learning models to accurately recognize signs captured through a camera. This innovation aims to enhance inclusivity and accessibility, with potential future expansions to support multiple sign languages and improve translation capabilities.

Uploaded by

22r11a05t5
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Abstract

Sign Language to Text Converter

Communication is a fundamental human need, yet individuals with hearing and speech impairments
often face barriers in interacting with the hearing community.
Sign language serves as their primary mode of communication, but its understanding is not
widespread among non-signers. The Sign Language to Text Converter aims to bridge this
communication gap by leveraging artificial intelligence (AI) and computer vision techniques to
interpret sign language gestures and convert them into readable text in real-time.

The system utilizes a webcam or smartphone camera to capture hand and finger movements. A
deep learning model trained on a large dataset of sign language gestures is employed to recognize
and classify the signs. Techniques such as MediaPipe Hand Tracking, Convolutional Neural
Networks (CNNs), and Recurrent Neural Networks (RNNs) are integrated for accurate recognition.
The processed data is then converted into textual output, which can be displayed on a screen or
used as input for text-to-speech (TTS) systems.

This innovation benefits the deaf and mute community by providing an efficient way to communicate
with non-signers, enhancing inclusivity in education, workplaces, and daily interactions. The system
can be further expanded to support multiple sign languages and integrate with existing digital
communication tools. Future advancements may include real-time translation of complex sentences
and improved accuracy through larger and more diverse datasets.

The Sign Language to Text Converter is a step toward breaking communication barriers, fostering
inclusivity, and enhancing accessibility for individuals with disabilities. Its implementation has the
potential to create a more connected and understanding society.

Authors:

22R11A05R8 - N. Amulya
22R11A05T5 - Neelima Sanapathi
23R15A0529 - P. Pavani

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