A Novel Character Segmentation-Reconstruction Approach for License Plate Recognition

Khare, Vijeta; Shivakumara, Palaiahnakote; Seng Chan, Chee; Lu, Tong; Kim Meng, Liang; Hock Woon, Hon; Blumenstein, Michael

Abstract

Developing an automatic license plate recognition system that can cope with multiple factors is challenging and interesting in the current scenario. In this paper, we introduce a new concept called partial character reconstruction to segment characters of license plates to enhance the performance of license plate recognition systems. Partial character reconstruction is proposed based on the characteristics of stroke width in the Laplacian and gradient domain in a novel way. This results in character components with incomplete shapes. The angular information of character components determined by PCA and the major axis are then studied by considering regular spacing between characters and aspect ratios of character components in a new way for segmenting characters. Next, the same stroke width properties are used for reconstructing the complete shape of each character in the gray domain rather than in the gradient domain, which helps in improving the recognition rate. Experimental results on benchmark license plate databases, namely, MIMOS, Medialab, UCSD data, Uninsbria data Challenged data, as well as video databases, namely, ICDAR 2015, YVT video, and natural scene data, namely, ICDAR 2013, ICDAR 2015, SVT, MSRA, show that the proposed technique is effective and useful.

Citation

Khare, V., Shivakumara, P., Seng Chan, C., Lu, T., Kim Meng, L., Hock Woon, H., & Blumenstein, M. (2019). A Novel Character Segmentation-Reconstruction Approach for License Plate Recognition. Expert systems with applications, 131, 219-239. https://doi.org/10.1016/j.eswa.2019.04.030

• Journal Article Type: Article
• Acceptance Date: Apr 16, 2019
• Online Publication Date: May 2, 2019
• Publication Date: 2019-10
• Deposit Date: Feb 2, 2024
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 131
• Pages: 219-239
• DOI: https://doi.org/10.1016/j.eswa.2019.04.030