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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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Volume 10 | Issue 12
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19599
Article Published : 8066
Total Authors : 21328
Total Reviewer : 770
Total Countries : 147
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Published Paper Details
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| Paper Title: | A Survey on Sign Language Recognition : A Case Study |
| Authors Name: | Sameer Kumar Singh , Ruchi , Saurav Kumar , Tejas V Kangod , Harshitha P |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_188841
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| Published Paper Id: | IJRTI2401041 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | The project focuses on creating a new system that combines computer vision and machine learning for sign language recognition. Technologies like image processing and neural networks are used to develop the system. It allows extracting key points from the user's hands and body language. The system is designed to use technologies like machine learning to extract important details from the user's hand and body movements. The project uses OpenCV to process video files and capture hand gestures, providing real-time feedback. The model is designed to learn and recognize alphabetic signs. A user-friendly graphical interface is created using Tkinter to make it accessible to many users. The goal is to enable communication and learning through real-time sign language recognition. Key points: Uses computer vision and machine learning for sign language recognition Extracts key points from hands and body language Processes video to capture gestures and provide real-time feedback Recognizes alphabetic signs User-friendly interface for accessibility Enables communication and learning through real-time recognition |
| Keywords: | Sign language recognition Computer vision Image processing Machine learning Neural networks Hand gestures Body language Real-time feedback Alphabetic signs User interface Communication Learning |
| Cite Article: | "A Survey on Sign Language Recognition : A Case Study", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.238 - 241, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401041.pdf |
| Downloads: | 000205256 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2401041
Registration ID:188841
Published In: Volume 9 Issue 1, January-2024
DOI (Digital Object Identifier):
Page No: 238 - 241 Country: BENGALURU, KARNATAKA, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2401041 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2401041 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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