Real-time License Plate Recognition in Overweight Vehicle Balance System
Huong-Giang Doan
Huong-Giang Doan, Control and Automation Faculty, Electric Power University, Ha Noi, Viet
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on April 10, 2020. | PP: 615-619 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3079049620/2020©BEIESP | DOI: 10.35940/ijitee.F3079.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recently, license plate recognition has been become an attractive field in computer vision. Which consists some main steps such as: data collection, plate detection, character separation, character segmentation, characters recognition and character series connection. Many state-of-the-art methods have been proposed while almost these approaches utilize complex algorithm. That spends a large time cost to obtain competitive accuracy; and/or high equipment performance such as CPU, GPU, cameras and so on. In addition, almost recent methods have been not deployed and evaluated for an end-to-end real application. Such system still has to face with many challenges due to the time cost, accuracy of system, complex background, light condition, motion blur and so on. In this paper, we propose a new framework for deeply evaluate efficient of license plate recognition system. Then a real application is deployed in the overweight balance system. In this application, the license plate recognition system is integrated as a middle step in order to reduce not only labor but also automatic for an industrial balance system.
Keywords: License Plate Recognition, Deep Learning, Character Recognition, License Plate Detection, Number Plate Detection, Optimize Tree.
Scope of the Article: Pattern Recognition