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http://dx.doi.org/10.21289/KSIC.2022.25.6.1285

Lightweight Deep Learning Model of Optical Character Recognition for Laundry Management  

Im, Seung-Jin (Busan Transportation Corporation)
Lee, Sang-Hyeop (Dept. of Electronic Eng., Kyungsung University)
Park, Jang-Sik (Dept. of Electronic Eng., Kyungsung University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.25, no.6_3, 2022 , pp. 1285-1291 More about this Journal
Abstract
In this paper, we propose a low-cost, low-power embedded environment-based deep learning lightweight model for input images to recognize laundry management codes. Laundry franchise companies mainly use barcode recognition-based systems to record laundry consignee information and laundry information for laundry collection management. Conventional laundry collection management systems using barcodes require barcode printing costs, and due to barcode damage and contamination, it is necessary to improve the cost of reprinting the barcode book in its entirety of 1 billion won annually. It is also difficult to do. Recognition performance is improved by applying the VGG model with 7 layers, which is a reduced-transformation of the VGGNet model for number recognition. As a result of the numerical recognition experiment of service parts drawings, the proposed method obtained a significantly improved result over the conventional method with an F1-Score of 0.95.
Keywords
Lightweight Deep Learning; Optical Character Recognition; VGG Model; Mathematical Morphology;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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