Application-oriented license plate recognition (AOLPR) has been introduced recently. It splits major license plate recognition applications into three major categories: access control, traffic law enforcement, and road patrol according to different scopes of variables considered for different application scenarios. However, several important issues are not solved, and thus become the central concerns of this thesis. These issues include: (1) no comparison study to evaluate the performance of different approaches in making the three modules for plate detection, character segmentation and character recognition; (2) no cross comparison to study the performance of a module with settings for one application category but tested on a different application category; and (3) no video data available from the earlier AOLPR database, however videos are becoming standard data format offered by many camera systems. In addition to addressing these three issues, this research also modifies and improves the design of the three modules. Gaussian mixture models (GMM) with expectation maximization (EM) clustering is exploited for plate detection, and proven more effective than other approaches. The maximally stable extremal regions (MSER) is applied for character segmentation, enhancing the performance under varying illumination. Multilayer classifier is used for character recognition to reduce the misclassified cases. To deal with video streams, this thesis applies successive differencing to detect license plates. Experiments upon the latest AOLPR database show that the modified method is quite competitive to other approaches.

Application-oriented license plate recognition (AOLPR) has been introduced recently. It splits major license plate recognition applications into three major categories: access control, traffic law enforcement, and road patrol according to different scopes of variables considered for different application scenarios. However, several important issues are not solved, and thus become the central concerns of this thesis. These issues include: (1) no comparison study to evaluate the performance of different approaches in making the three modules for plate detection, character segmentation and character recognition; (2) no cross comparison to study the performance of a module with settings for one application category but tested on a different application category; and (3) no video data available from the earlier AOLPR database, however videos are becoming standard data format offered by many camera systems. In addition to addressing these three issues, this research also modifies and improves the design of the three modules. Gaussian mixture models (GMM) with expectation maximization (EM) clustering is exploited for plate detection, and proven more effective than other approaches. The maximally stable extremal regions (MSER) is applied for character segmentation, enhancing the performance under varying illumination. Multilayer classifier is used for character recognition to reduce the misclassified cases. To deal with video streams, this thesis applies successive differencing to detect license plates. Experiments upon the latest AOLPR database show that the modified method is quite competitive to other approaches.

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