Alphabetic hand sign interpretation using geometric invariance

Adhan, Suchin; Pintavirooj, C.

doi:10.1109/bmeicon.2014.7017426

“…Here, the performance is evaluated with 1300 hand gestures and yields better results. In [25], a hand gesture interpretation technique is employed using B spline curvature concept and geometric invariance method for recognizing 24 ASL alphabets gestures captured by web camera and offers satisfactory results. Recognition system of ASL finger spelling with phonological feature-based tandem models is developed in [26], using Hidden Morkov Model (HMM)-based baseline with Gaussian mixture observation distributions.…”

Section: Related Workmentioning

confidence: 99%

American Sign Language Recognition System: An Optimal Approach

Shivashankara¹,

Srinath²

2018

IJIGSP

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The Sign language is a visual language used by the people with the speech and hearing disabilities for communication in their daily conversation activities. It is completely an optical communication language through its native grammar, be unlike fundamentally from that of oral languages. In this research paper, presented an optimal approach, whose major objective is to accomplish the transliteration of 24 static sign language alphabets and numbers of American Sign Language into humanoid or machine decipherable English manuscript. Pre-processing operations of the signed input gesture are done in the first phase. In the next phase, the various region properties of pre-processed gesture image is computed. In the final phase, based on the properties calculated of earlier phase, the transliteration of signed gesture into text has been carried out. This paper also presents the statistical result evaluation with the comparative graphical depiction of existing techniques and proposed technique.

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Thai finger spelling localization and classification under complex background using a YOLO-based deep learning

Nakjai

¹

,

Maneerat

²

,

Katanyukul

³

2019

Proceedings of the 11th International Conference on Computer Modeling and Simulation

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American Sign Language Recognition System: Enhancing Communication Accessibility for Individuals with Hearing Disabilities

Soumay Patidar,

Prof. (Dr.) Prashant Lakkadwala

2024

IJARSCT

0

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The Sign language is a visual language used by the people with the speech and hearing disabilities for communication in their daily conversation activities. It is completely an optical communication language through its native grammar, be unlike fundamentally from that of oral languages. In this research paper, presented an optimal approach, whose major objective is to accomplish the transliteration of 24 static sign language alphabets and numbers of American Sign Language into humanoid or machine decipherable English manuscript. Pre-processing operations of the signed input gesture are done in the first phase. In the next phase, the various region properties of pre-processed gesture image is computed. In the final phase, based on the properties calculated of earlier phase, the transliteration of signed gesture into text has been carried out. This paper also presents the statistical result evaluation with the comparative graphical depiction of existing techniques and proposed technique

show abstract

Alphabetic hand sign interpretation using geometric invariance

Cited by 3 publications

References 2 publications

American Sign Language Recognition System: An Optimal Approach

American Sign Language Recognition System: An Optimal Approach

Thai finger spelling localization and classification under complex background using a YOLO-based deep learning

American Sign Language Recognition System: Enhancing Communication Accessibility for Individuals with Hearing Disabilities

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