Paper Info
Title
A new feature extraction method for license plate recognition
Abstract
In this paper, character recognition found in license plates is described. The developed procedure is based on real license plates. The numbers are limited to ten classes (0-9). The character recognition problem is a very important problem and many people worked on implementing different methods. One of the successful set of methods to recognize characters from a closed set are the methods which uses lines, but this method suffers from the fact that the number of lines and the thresholds for each feature in each line are selected manually for each set of characters. Our goal is being able to develop the optimal recognition tree in the classification process automatically. Several phases are needed in order to recognize a character. In the feature extraction phase, we introduce two new features; the first feature is related to the quantization process on a specific feature, and the second feature is the combination of several features to form new features. The developed algorithm was applied to different datasets in license plates from KSA; and the recognition rate was above 95%. In this paper, we are concerned on the English Numbers in the KSA license plates.
Year
DOI
Venue
2015
10.1109/DICTAP.2015.7113172
DICTAP
Keywords
Field
DocType
license plate recognition, feature extraction, quantization, feature selection, recognition tree
Computer vision,Character recognition,Pattern recognition,Computer science,Feature (computer vision),Feature extraction,Closed set,Feature (machine learning),Artificial intelligence,Quantization (signal processing),License
Conference
ISBN
Citations 
PageRank 
978-1-4799-4130-8
0
0.34
References 
Authors
1
5
Name
Order
Citations
PageRank
Salah Al-Shami100.34
Ali El Zaart2528.17
Rached Zantout3187.67
Ahmed S. Zekri4378.91
Khaled M. Almustafa500.34