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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
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| Paper Title: | Design & Development of Hands Sign and Gesture Recognition |
| Authors Name: | Rachit Sharma , Anuj Kumar , Gaurav kumar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_200453
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| Published Paper Id: | IJRTI2505281 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | The hand gesture is one of the non-verbal communication methods used in sign language. It is most used by people with speech or hearing impairments to communicate with non-disabled people and with each other. Numerous manufacturers worldwide have developed diverse sign language systems; however, they lack adaptability and affordability for end users. Thus, to help deaf and dumb people communicate with others more effectively, the "Hand sign and gesture recognition system software" proposed in this proposal provides a system prototype that can automatically understand sign language. In sites like "YouTube" videos where there is currently no feature for automatic text generation because of gestures, this approach can also be employed, likewise sign languages. Research on gesture recognition is still in its early stages. Hand gestures are vital to everyday life and play a significant role in nonverbal communication. The software's goal is to demonstrate a real-time system for hand gesture and sign recognition by detecting certain shape-based features, such as orientation, the centroid of the Centre of Mass, the status of the fingers, and the thumb in positions where the hand's fingers are raised or folded. However, Convolutional Neural Networks (CNNs) will handle the feature extraction process entirely. Every frame of the video will be captured in this process, and each frame will be used to locate hands and clip them out so that our CNN may use them as input. We used ISL as a case study for our purposes. The back projection histogram approach was employed in this model to set the image's histogram. We used CNNs for training and testing, and as a result, our test accuracy was 99.89%. Our model's independence from external hardware or devices is one of its benefits. |
| Keywords: | Convolutional Neural Network (CNN) |
| Cite Article: | "Design & Development of Hands Sign and Gesture Recognition ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c697-c703, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505281.pdf |
| Downloads: | 000379 |
| 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: IJRTI2505281
Registration ID:200453
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: c697-c703 Country: meerut, Uttar Pradesh, India Research Area: Master of Computer Application Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505281 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505281 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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