UGC Approved Journal no 63975(19)

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Volume 10 Issue 11
November-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2311332


Registration ID:
528081

Page Number

d252-d255

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Title

Real-Time Conversion Of Sign Language To Text And Speech And Vice-Versa

Abstract

This research focuses on the development of a real-time system for the conversion of sign language into text and speech, and vice-versa, to facilitate seamless communication between individuals with hearing impairments and those without. The proposed system integrates advanced technologies such as Convolutional Neural Networks (CNN), image processing, and animation gesture recognition to achieve accurate and efficient translation between sign language and spoken language. The use of CNN plays a pivotal role in the recognition of sign language gestures from video input. The model is trained on a diverse dataset of sign language gestures, allowing it to learn and generalize the intricate hand movements and expressions inherent in sign language communication. This deep learning approach enhances the system's ability to recognize a wide range of gestures with high accuracy. Image processing techniques are employed to preprocess the video input, extracting relevant features and reducing noise to enhance the overall performance of the system. The integration of image processing not only contributes to the accuracy of gesture recognition but also ensures robustness in varying lighting conditions and background environments. A distinctive feature of the system lies in the incorporation of animation gesture recognition, which involves simulating real gestures for recognition. This simulation not only aids in accurately capturing the nuances of sign language gestures but also enhances the overall user experience. The system utilizes these animated representations for recognition and subsequently converts them into text or speech, providing a dynamic and expressive layer to the communication process. The real-time nature of the system ensures minimal latency in the translation process, enabling instantaneous communication between individuals using sign language and those relying on spoken language. The proposed solution holds promise in breaking down communication barriers and fostering inclusivity in various social and professional settings. Sign Language, Convolutional Neural Network (CNN), Image processing, Natural Language Processing (NLP), Text-to-Speech (TTS)

Key Words

Sign Language, Convolutional Neural Network (CNN), Image processing, Natural Language Processing (NLP), Text-to-Speech (TTS)

Cite This Article

"Real-Time Conversion Of Sign Language To Text And Speech And Vice-Versa", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.d252-d255, November-2023, Available :http://www.jetir.org/papers/JETIR2311332.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Real-Time Conversion Of Sign Language To Text And Speech And Vice-Versa", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppd252-d255, November-2023, Available at : http://www.jetir.org/papers/JETIR2311332.pdf

Publication Details

Published Paper ID: JETIR2311332
Registration ID: 528081
Published In: Volume 10 | Issue 11 | Year November-2023
DOI (Digital Object Identifier):
Page No: d252-d255
Country: Pune, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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