Enhancing Communication Accessibility: A Deep Learning Approach to Gesture Recognition for the Deaf and Mute Community

被引:0
|
作者
Kandula, Ashok Reddy [1 ]
Ramachandran, Priya Darshini [1 ]
Tummalapalli, Nikhitha [1 ]
Tirukkovalluri, Krishna Priya [1 ]
Kothapalli, Kavya [1 ]
机构
[1] Seshadri Rao Gudlavalleru Engn Coll, Dept AI & DS, Gudlavalleru, India
关键词
Recurrent Neural Network; long short-term memory; OpenCV; Media pipe; Rectified Linear Unit; SoftMax; Adam Optimizer; Gesture Classification;
D O I
10.1109/ICPCSN62568.2024.00142
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Though human communication is crucial, individuals with physical obstacles like deafness and naivety frequently find it challenging to communicate effectively. Utilizing sign language can be a primitive component of communication among this group. This paper suggests a creative approach to closing the communication hole by engaging the comprehension of sign dialect without the prerequisite for people to memorize it. Tallying flag recording on webcams gives a successful and straightforward means of collecting data. These hand advancements are meticulously recorded and utilized as the primitive for planning and testing models. The credibility of pushing for inclusivity exists, as does the potential for making more effective communication channels that cater to assorted social classes. Sign tongue isn't a burdensome errand due to the effortlessness of this procedure, which makes communication less demanding. Through this unused approach, social instinct will progress through predominant communication, frequent understanding, and support among particular levels of society. In this way, this movement has the potential to begin noteworthy updates in interpersonal associations, develop inclusivity, and address existing boundaries for fruitful communication. This research work attempted it more than once and were attained a better accuracy of 90%.
引用
收藏
页码:842 / 849
页数:8
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