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Benchmark for license plate character segmentation

[+] Author Affiliations
Gabriel Resende Gonçalves, William Robson Schwartz

Universidade Federal de Minas Gerais, Smart Surveillance Interest Group, Department of Computer Science, Avenida Antônio Carlos No. 6627, Belo Horizonte, Minas Gerais 31270010, Brazil

Sirlene Pio Gomes da Silva

Universidade Federal de Ouro Preto, Computing Department, Morro do Cruzeiro campus, Ouro Preto, Minas Gerais 35400000, Brazil

David Menotti

Universidade Federal do Paraná, Department of Informatics, St. XV de Novembro 1299, Curitiba, Paraná 81531980, Brazil

J. Electron. Imaging. 25(5), 053034 (Oct 24, 2016). doi:10.1117/1.JEI.25.5.053034
History: Received June 26, 2016; Accepted September 28, 2016
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Abstract.  Automatic license plate recognition (ALPR) has been the focus of many researches in the past years. In general, ALPR is divided into the following problems: detection of on-track vehicles, license plate detection, segmentation of license plate characters, and optical character recognition (OCR). Even though commercial solutions are available for controlled acquisition conditions, e.g., the entrance of a parking lot, ALPR is still an open problem when dealing with data acquired from uncontrolled environments, such as roads and highways when relying only on imaging sensors. Due to the multiple orientations and scales of the license plates captured by the camera, a very challenging task of the ALPR is the license plate character segmentation (LPCS) step, because its effectiveness is required to be (near) optimal to achieve a high recognition rate by the OCR. To tackle the LPCS problem, this work proposes a benchmark composed of a dataset designed to focus specifically on the character segmentation step of the ALPR within an evaluation protocol. Furthermore, we propose the Jaccard-centroid coefficient, an evaluation measure more suitable than the Jaccard coefficient regarding the location of the bounding box within the ground-truth annotation. The dataset is composed of 2000 Brazilian license plates consisting of 14000 alphanumeric symbols and their corresponding bounding box annotations. We also present a straightforward approach to perform LPCS efficiently. Finally, we provide an experimental evaluation for the dataset based on five LPCS approaches and demonstrate the importance of character segmentation for achieving an accurate OCR.

© 2016 SPIE and IS&T


Gabriel Resende Gonçalves ; Sirlene Pio Gomes da Silva ; David Menotti and William Robson Schwartz
"Benchmark for license plate character segmentation", J. Electron. Imaging. 25(5), 053034 (Oct 24, 2016). ;

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