Effect of thinning extent on ASL number recognition using open-finger distance feature measurement technique

Thalange, Asha; Dixit, Shantanu K.

doi:10.1109/spaces.2015.7058299

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…12 signed gestures were unsuccessful to recognize evidently in usual background which provides a recognition rate of 76% using Hu moment classification. In [13] [16], and obtained 92.09% of recognition rate.…”

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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Section: Related Workmentioning

confidence: 99%

American Sign Language Recognition System: An Optimal Approach

Shivashankara¹,

Srinath²

2018

IJIGSP

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Real time human pose recognition using leap motion sensor

Mapari¹,

Kharat²

2015

2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

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Breaking the silence: empowering the mute-deaf community through automatic sign language decoding

Rahman Mohamed

2024

Biomedical Engineering / Biomedizinische Technik

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Objectives The objective of this study is to develop a system for automatic sign language recognition to improve the quality of life for the mute-deaf community in Egypt. The system aims to bridge the communication gap by identifying and converting right-hand gestures into audible sounds or displayed text. Methods To achieve the objectives, a convolutional neural network (CNN) model is employed. The model is trained to recognize right-hand gestures captured by an affordable web camera. A dataset was created with the help of six volunteers for training, testing, and validation purposes. Results The proposed system achieved an impressive average accuracy of 99.65 % in recognizing right-hand gestures, with high precision value of 95.11 %. The system effectively addressed the issue of gesture similarity between certain alphabets by successfully distinguishing between their respective gestures. Conclusions The proposed system offers a promising solution for automatic sign language recognition, benefiting the mute-deaf community in Egypt. By accurately identifying and converting right-hand gestures, the system facilitates communication and interaction with the wider world. This technology has the potential to greatly enhance the quality of life for individuals who are unable to speak or hear, promoting inclusivity and accessibility.

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Effect of thinning extent on ASL number recognition using open-finger distance feature measurement technique

Cited by 3 publications

References 10 publications

American Sign Language Recognition System: An Optimal Approach

American Sign Language Recognition System: An Optimal Approach

Real time human pose recognition using leap motion sensor

Breaking the silence: empowering the mute-deaf community through automatic sign language decoding

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