![]() ![]() In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. Lee, Y., Song, T., Ku, B., Jeon, S., Han, D.K., Ko, H.: License plate detection using local structure patterns. Hsu, G.S., Chen, J.C., Chung, Y.Z.: Application-oriented license plate recognition. Gonçalves, G.R., da Silva, S.P.G., Menotti, D., Schwartz, W.R.: Benchmark for license plate character segmentation. In: IEEE International Conference on Computer Vision, pp. 18(9), 2351–2363 (2017)Ĭheng, Z., Bai, F., Xu, Y., Zheng, G., Pu, S., Zhou, S.: Focusing attention: towards accurate text recognition in natural images. Preprint arXiv:2004.10934 (2020)īulan, O., Kozitsky, V., Ramesh, P., Shreve, M.: Segmentation-and annotation-free license plate recognition with deep localization and failure identification. Especially in sub-datasets like CCPD-Base, CCPD-DB, CCPD-FN, CCPD-Weather, and CCPD-Challenge, the recognition accuracy achieved 99.2%, 98.1%, 98.5%, 97.8%, and 86.2%, respectively.īochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: Optimal speed and accuracy of object detection. The test results indicate that our proposed algorithm performs well in a variety of complex scenarios. In the second stage, the ILPRNET license plate recognition network is used to perform localization of license plate characters and the 2D attentional-based license plate recognizer with an CNN encoder is capable of recognizing license plates accurately. In the first stage, YOLOv3 is adopted to detect the position of the license plate and then extract the license plate. Therefore, this paper proposes a two-stage license plate recognition algorithm based on YOLOv3 and Improved License Plate Recognition Net (ILPRNET). In complex natural scenes such as CCPD-DB, CCPD-FN, CCPD-Rotate, CCPD-Tile, CCPD-Weather, and CCPD-Challenge from the Chinese City Parking Dataset (CCPD), inaccurate localization and poor character recognition accuracy issues appear towards existing license plates. Most applications are currently focused on good conditions. This paper is concerned with the detection and recognition of Chinese license plates in complex backgrounds. ![]()
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