License plate recognition is widely used in our daily life. Image binarization, which is a process to convert an image to white and black, is an important step of license plate recognition. Among the proposed binarization methods, Otsu method is the most famous and commonly used one in a license plate recognition system since it is the fastest and can reach a comparable recognition accuracy. The main disadvantage of Otsu method is that it is sensitive to luminance effect and noise, and this property is impractical since most vehicle images are captured in an open environment. In this paper, we propose a system to improve the performance of automatic license plates reorganization in the open environment in Taiwan. Our system uses a binarization method which is inspired by the symmetry principles. Experimental results showed that when our method has a similar time complexity to that of Otsu, our method can improve the recognition rate up to 1.30 times better than Otsu.

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In recent years, some researches on free text authentication by keystroke dynamics have been proposed. The main problem of these proposed researches is the requirement of a very long training time. To increase users' willingness to use the proposed authentication system, one simple and feasible way is shortening the training time. In this case, the training data is limited and is known as the limited resource problem. In this paper, we propose new soft biometrics and a new classifier for limited resources in free text authentication in English. Our new soft biometrics combines the idea of data mining and statistical prediction. Because our soft biometric is a mining result of limited resources, it is sensitive to outliers and a traditional statistical classifier cannot be applied. To solve this problem, the proposed classifiers considered the problem of an outlier and calculated the difference of cluster distribution. There are 114 participants in our experiments. Experimental results show that our approach can improve the accuracy of free text authentication in the case of limited resources.