Authors:
Abdelhalim Boutarfa
1
;
Mahfoud Hamada
1
and
Emptoz Hubert
2
Affiliations:
1
University of Batna, Algeria
;
2
LIRIS, France
Keyword(s):
Intelligent Transportation Systems, License plate, Feature extraction, Segmentation, Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
To solve the problems of heavy traffic, due to the increase in the number of vehicles, modern cities need to establish effectively automatic systems for traffic monitoring and management. One of the most useful systems is the License-Plate Recognition System which captures images of vehicles and reads the plate’s registration numbers automatically. Our method in this paper presents a robust algorithm for segmenting and recognizing a vehicle license plate area from a road image. As preprocessing steps, we statistically analyze the features of some sample plate images, and compute thresholds for each feature to decide whether a pixel is inside a plate or we cannot decide it. Our methodology starts from constructing the binary version of a road image according to the thresholds. Then, we select at most three strong candidate areas by searching the binary image with a moving window. The plate area is selected among the candidates with simple heuristics. Our algorithm is stable and robust
against the cases of plate transformation and/or decolorization. The experimental results show 98.05% of successful plate recognition for 256 input images.
(More)