A Study on the Product Classification System of New-Hanbok Online Shopping Mall

2020 ◽  
Vol 23 (3) ◽  
pp. 79-93
Author(s):  
Jung-Eun Lee ◽  
Min-Jung Chang
2018 ◽  
Vol 7 (3.33) ◽  
pp. 179
Author(s):  
Jae-Kyung Sung ◽  
Sang-Min Park ◽  
Sang-Yun Sin ◽  
Yung Bok Kim

This paper proposes a product classification system based on deep learning using Korean character images (Hangul) to search for products in the shopping mall. Generally, an online shopping mall customer searches through a category classification or a product name to purchase a product. When the exact product name or category is not clear, the user has to search its name. However, the product image classification is degraded because the product logos and characters in the package often interfere. To solve such problems, we propose a classification system based on Deep Learning using Korean character images. The learning data of this system uses Korean character images of PHD08, a Hangul (Korean-language) database. The experimental is carried out using product names collected on the web. For the performance experiment, 10 categories of online shopping mall are selected and the classification accuracy is measured and compared with the previous systems. 


Author(s):  
Eun-Young Jang ◽  
Sang-Joon Lee ◽  
Kyeong-Rak Lee ◽  
Sang-Hyun Lee

2018 ◽  
Vol 19 (6) ◽  
pp. 91-108
Author(s):  
Hyeog In Kwon ◽  
Bo Hyun Baek ◽  
Yong Su Jeon ◽  
Yea Jin Ahn

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