Segmentation of characters on car license plates

License plate recognition usually contains three steps, namely license plate detection/localization, character segmentation and character recognition. When reading characters on a license plate one by one after license plate detection step, it is crucial to accurately segment the characters. The segmentation step may be affected by many factors such as license plate boundaries (frames). The recognition accuracy will be significantly reduced if the characters are not properly segmented. This paper presents an efficient algorithm for character segmentation on a license plate. The algorithm follows the step that detects the license plates using an AdaBoost algorithm. It is based on an efficient and accurate skew and slant correction of license plates, and works together with boundary (frame) removal of license plates. The algorithm is efficient and can be applied in real-time applications. The experiments are performed to show the accuracy of segmentation. © 2008 IEEE

A hierarchically combined classifier for license plate recognition

High accuracy and fast recognition speed are two requirements for real-time and automatic license plate recognition system. In this paper, we propose a hierarchically combined classifier based on an Inductive Learning Based Method and an SVM-based classification. This approach employs the inductive learning based method to roughly divide all classes into smaller groups. Then the SVM method is used for character classification in individual groups. Both start from a collection of samples of characters from license plates. After a training process using some known samples in advance, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for further fast training and testing processes for SVMbased classification. Experimental results for the proposed approach are given. From the experimental results, we can make the conclusion that the hierarchically combined classifier is better than either the inductive learning based classification or the SVMbased classification in terms of error rates and processing speeds. © 2008 IEEE

BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM

Object Recognition has been a field of interest to many researchers. In fact, it has been referred to as the most important problem in machine or computer vision. Researchers have developed many algorithms to solve the problem of object recognition that are machine vision motivated. On the other hand, biology has motivated researchers to study the visual system of humans and animals such as monkeys and map it into a computational model. Some of these models are based on the feed-forward mechanism of information communication in cortex where the information is communicated between the different visual areas from the lower areas to the top areas in a feed-forward manner; however, the performance of these models has been affected much by the increase of clutter in the scene as well as occlusion. Another mechanism of information processing in the cortex is called the feedback mechanism, where the information from the top areas in the visual system is communicated to the lower areas in a feedback manner; this mechanism has also been mapped into computational models. All these models which are based on the feed-forward or feedback mechanisms have shown promising results. However, during the testing of these models, there have been some issues that affect their performance such as occlusion that prevents objects from being visible. In addition, scenes that contain high amounts of clutter in them, where there are so many objects, have also affected the performance of these models. In fact, the performance has been reported to drop to 74% when systems that are based on these models are subjected to one or both of the issues mentioned above. The human visual system, naturally, utilizes both feed-forward and feedback mechanisms in the operation of perceiving the surrounding environment. Both feed-forward and feedback mechanisms are integrated in a way that makes the visual system of the human outperforms any state-of-the-art system. In this research, a proposed model of object recognition based on the integration concept of the feed-forward and feedback mechanisms in the human visual system is presented

Number plate recognition based on support vector machines

Automatic number plate recognition method is required due to increasing traffic management. In this paper, we first briefly review some knowledge of Support Vector Machines (SVMs). Then a number plate recognition algorithm is proposed. This algorithm employs an SVM to recognize numbers. The algorithm starts from a collection of samples of numbers from number plates. Each character is recognized by an SVM, which is trained by some known samples in advance. In order to recognize a number plate correctly, all numbers are tested one by one using the trained model. The recognition results are achieved by finding the maximum value between the outputs of SVMs. In this paper, experimental results based on SVMs are given. From the experimental results, we can make the conclusion that SVM is better than others such as inductive learning-based number recognition © 2006 IEEE