Classification of Benjapakee Buddha amulets image by deep learning

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Narut Butploy
Sangthong Boonying

Abstract

          Buddha amulet is a symbol of belief and religion, which has a magical power and rare sacred object. Some Buddha amulets can only be seen by those who have not studied cannot know the name or identify the Buddha amulet. Nowadays, the image recognition using machine learning technology has developed rapidly. Deep learning is one of the technology that helps computers to recognize images precisely. The development of appropriate model for the image recognition might lead to create an automatic system for classification of Benjapakee Buddha amulets. The research presented the applied Convolutional Neural Network system (CNN) model of deep learning to classify the Benjapakee Buddha Amulets images. The aim of the research was to develop this suitable architecture deep learning for recognizing the Benjapakee Buddha amulets accurately for the people who did not know and identify these amulets. The experiment consisted of the collection of 100 amulet images for each type, a total of 500 images, and then the test was conducted with a fitty new sample. The results showed that the 3-layer convolution architecture produced the best results. The efficiency of this model could correctly identify 80% of Phra Somdej, Phra Nang Phaya, Phra Rod and Phra Somgor, and 70% of Phra Phong images. The differences between the images of the Benjapakee Buddha amulet of the Buddha image have distinct characteristics, making the ability to accurately identify the images. However, the accuracy of the test depended on the number of tested sample images. The proposed model may only be suitable for images tested, so those interested in image recognition may have to further develop a model suitable for the images.

Article Details

How to Cite
Butploy, N., & Boonying, S. (2020). Classification of Benjapakee Buddha amulets image by deep learning. RMUTSB ACADEMIC JOURNAL, 8(1), 100–111. Retrieved from https://li01.tci-thaijo.org/index.php/rmutsb-sci/article/view/231813
Section
Research Article

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