Please use this identifier to cite or link to this item: https://publication.npru.ac.th/jspui/handle/123456789/1330
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dc.contributor.authorGedkhaw, Eakbodin-
dc.date.accessioned2021-08-20T08:22:33Z-
dc.date.available2021-08-20T08:22:33Z-
dc.date.issued2021-07-08-
dc.identifier.urihttps://publication.npru.ac.th/jspui/handle/123456789/1330-
dc.description.abstractIn this paper propose 2D Convolutional Neural Networks in Thai sign language recognition. Network was train by end-to-end for continuous gesture recognition. 2D convolutions was choose to extract features related to the gestures of Thai sign language. The design of the 2D CNN model and the generate of Thai sign language gesture data set, using 2D convolution and pooling layers, was used to train differentiation of the data. In experiment use 3 Thai sign language gestures: "Hello", "Love" and "Sick", 1000 images of each gesture, total 3000 images were selected in training and used 100 images of each gesture, total of 300 images were selected to test. The experiment shown that the designed model can greatly enhance the gesture perception, accuracy value is 0.93 and loss value is 0.27 obtained from 2D CNNs. The total learn time is 1 hr 15 min 46 sec.en_US
dc.publisherThe 13th NPRU National Academic Conference Nakhon Pathom Rajabhat Universityen_US
dc.subject2D Convolution Neural Networken_US
dc.subjectSign Language Recognitionen_US
dc.subjectDeep Learningen_US
dc.titleThe Performance of Thai Sign Language Recognition Using 2D Convolutional Neural Networksen_US
dc.title.alternativeประสิทธิภาพการรู้จำภาษามือไทยโดยใช้เครือข่ายประสาทเทียมแบบคอนโวลูชัน 2 มิติen_US
Appears in Collections:Proceedings of the 13th NPRU National Academic Conference

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