Paper
15 November 1999 Character segmentation algorithm for recognition of vehicle license plate
JennKwei Tyan, Claus Neubauer, Ljubisa Goganovic
Author Affiliations +
Proceedings Volume 3838, Mobile Robots XIV; (1999) https://doi.org/10.1117/12.369260
Event: Photonics East '99, 1999, Boston, MA, United States
Abstract
In the automated license plate recognition system, many reading errors are caused by inadequate character segmentation. In particular, character segmentation becomes difficult as the acquired vehicle images are seriously degraded. In this paper, we use computer vision techniques and propose a recognition-based segmentation method coupled with template matching and neural network. This will enhance the accuracy of the recognition system that aims to read automatically the German license plate. Algorithmic improvements for a projection-based segmentation are described here. In the preprocessing stage, plate tilt detection and position refinement methods are developed to prepare the data for later process. For separating touching characters, a discrimination function is presented based on a differential analysis of character contour distance. A conditionally recursive segmentation with the feedback of recognition is developed for effectively splitting touching characters and merging broken characters. We have implemented our algorithms in the intelligent camera system and obtained improvement for the recognition rate. Currently, the experiment conducted with greatly varying illumination conditions is shown at the recognition rate of an average of 92 %. Further improvement of the system is continuously undertaken for various data sets acquired under different environment conditions.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
JennKwei Tyan, Claus Neubauer, and Ljubisa Goganovic "Character segmentation algorithm for recognition of vehicle license plate", Proc. SPIE 3838, Mobile Robots XIV, (15 November 1999); https://doi.org/10.1117/12.369260
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KEYWORDS
Image segmentation

Detection and tracking algorithms

Optical character recognition

Image processing

Imaging systems

Neural networks

Cameras

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