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Sign language is composed of various gestures formed by different shapes of the hand, its movements, orientations, and facial expressions. These gestures are generally used by deaf and dumb people in order to express their thought. The sign language in use at a particular place depends on the culture and spoken language at that place. ISL is a standard and well-developed way of communication for hearing-impaired people in India. The classification and recognition of the Indian sign language provided in real-time by the dumb-deaf user are the primary goals of the suggested approach. Thus, the algorithm's speed and simplicity are crucial. The system approach entails segmenting the hand based on skin color data, converting that segmented image to binary, and then using feature extraction to extract features. The system is able to detect the hand signs and convert them to both text and speech. In early times, many gesture recognition techniques have been developed for recognizing and tracking various hand gestures, all have their advantages and drawbacks. We proposed a simple shape-based approach for hand gesture recognition with several procedures, such as orientation detection, thumb detection, smudges elimination etc.
Keywords:
Indian Sign Lnguage(ISL), segmentation, feature Extraction, gesture recognition
Cite Article:
"AI based Robust Indian Sign Language Recognition System using Shape Features ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.d151-d155, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505321.pdf
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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