| Summary: | 碩士 === 國立臺灣師範大學 === 電機工程學系 === 105 === In recent years, the use of license plate recognition technology in traffic monitor has attracted a lot of attention because it can be used in a smart city to do criminal investigation and traffic detection. License plate recognition technology has been widely used in parking lot management systems which has fixed shooting angle and lighting environments. The license plate recognition used in traffic monitor will encounter difficulties in character recognition due to factors such as shooting angle, vehicle speed and environment light and shadow. In addition to the above environmental conditions, the common challenges of character recognition include: license plate fuzzy, dirty, different fonts, similar characters and other changes.
This paper presents a license plate identification system with SVM classifier as the core. The system is divided into three parts, including license plate detection, character segmentation and character recognition.
License plate detection section, we use the Support Vector Machine (SVM) classifier. The purpose of the SVM classifier is to classify the license plate and the non-license plate area, and this study uses the SVM classifier with histogram of oriented gradient (HOG) as the training feature. In order to reduce the computing time, To save the computation time, we use graphics processor units (GPU) to accelerate SVM calculations. Experimental results show that our system in the three lanes with 97.69% license plate detection rate. After seizing license plates, character segmentation is adopted to separate characters. In this stage, we through the horizontal projection method to remove the other non-character arrangement om the license plate, and then use the vertical projection method to separete the license plate into characters.
Finally in the last stage of character recognition, this paper presents a hierarchical architecture combining supervised K-means and support vector machine. The supervised K-means is used to classify characters into subgroups. The characters of subgroups can be further classified by support vector machine. The advantage of the proposed approach is to reduce the classes of characters in each subgroup to further reduce the number of SVMs and their complexity, and thus improve the accuracy of character recognition. Experimental results show that our proposed hierarchical architecture achieves an accuracy of 98.89% in character recognition. Compared with the license plate recognition technology using SVM alone, we get a 3.6% improvement in recognition rate.
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