Benchmark for License Plate Character Segmentation

• Main Authors: Gonçalves, Gabriel Resende, da Silva, Sirlene Pio Gomes, Menotti, David, Schwartz, William Robson
• Format: Journal Article
• Language: English
• Published: 31-10-2016
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.1607.02937

J. Electron. Imaging. 25(5), 053034 (Oct 24, 2016) 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 plates detection, segmention
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, which 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
novel 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, a new 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
2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and
their corresponding bounding box annotations. We also present a new
straightforward approach to perform LPCS efficiently. Finally, we provide an
experimental evaluation for the dataset based on four LPCS approaches and
demonstrate the importance of character segmentation for achieving an accurate
OCR.