Iranian License Plate Recognition using Deep Learning

Atefeh Ranjkesh Rashtehroudi, A. Shahbahrami, Alireza Akoushideh
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引用次数: 5

Abstract

Automated License Plate Recognition (ALPR) has many applications in intelligent transport system. The ALPR has three main steps, License Plate (LP) localization, segmentation and Optical Character Recognition (OCR). Each step needs different techniques in real condition and each technique has its specific characteristics. The LP localization techniques detect the LP after that segmentation algorithms should segment and isolate each character from each other. Finally, the OCR step is applied to recognize the separated characters. The final accuracy depends on the accuracy of each step. To improve the OCR step performance, we combine both segmentation and OCR steps as a single-stage using deep learning techniques such as the You Only Look Once (YOLO) framework. Our experimental results show that this proposed approach recognizes the Iranian LP characters with accuracy 99.2% compared to previous works.
伊朗车牌识别使用深度学习
车牌自动识别技术在智能交通系统中有着广泛的应用。ALPR有三个主要步骤:车牌定位、分割和光学字符识别(OCR)。每个步骤在实际条件下都需要不同的技术,每种技术都有其特定的特点。LP定位技术对LP进行检测,然后分割算法对每个字符进行分割和分离。最后,应用OCR步骤对分离字符进行识别。最终的精度取决于每一步的精度。为了提高OCR步骤的性能,我们使用深度学习技术(如You Only Look Once (YOLO)框架)将分割和OCR步骤结合为一个单阶段。实验结果表明,该方法对伊朗语LP字符的识别准确率达到99.2%。
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