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URN etd-0712105-213821 Statistics This thesis had been viewed 3327 times. Download 6467 times. Author Yan-Bin Chen Author's Email Address RobinChen@via.com.tw Department Information Management Year 2004 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 123 Title A STUDY FOR APPLYING REVERSE LOGIC ARTIFICIAL NEURAL NETWROK IN CAR LICENSE PLATE CHARACTER RECOGNITION Keyword Reverse Logic License plate character recognition Back-propagation neural network Back-propagation neural network License plate character recognition Reverse Logic Abstract Over the recent years, as the vehicle license plate recognition system technology continues to breakthrough, the actually potential applications are also on the rise. The common examples are detecting stolen cars ticketing traffic violations, and the toll collecting systems of man-free parking lots. Yet in practical implementation, a main reason that the license plate recognition system is unable to rapidly and expeditiously become prevalent lies in how the recognition technology leaves room to be improved upon, such as the license plate positioning ratio, character recognition ratio and so forth. As a result, the study intends to focus on this area in anticipation that the recognition accuracy of the license plate recognition system can be further enhanced.
This study utilizes back-propagation neural network (BPNN) as the recognition system tool. However, the back-propagation neural network does have a few deficiencies that need to be improved upon, such as the issues of a slow learning speed in the training process are prone to lead to partial minimum values that are difficult to converge, and the need to retrain an enormous volume of data whenever new training samples are added or deleted, which tends to crate significant obstacles in practical implementation. In light of that, this study intends to focus on the conventional back-propagation neural network’s learning paradigm and network framework combining normalization, parallel dispersed and reverses logical thinking concepts to propose a reverse logical artificial neural network (RLANN). The RLANN is intended to improve some of the back-propagation neural network’s problems of a slow learning speed in training that makes normalization difficult, and the requirement for a fixed amount of training samples. By the way, a practical validation of the license plate character recognition system is conducted to validate the performance improvement.
The study has only adopted 45 character samples to train the reverse logic artificial neural network, and employed 200 license plates as test samples. The overall license plate recognition ratio reaching 91%, and the overall character recognition ratios of 98.5% have validated the results to be satisfactory.
Advisor Committee Yu-Chung Hung - advisor
Ming-Guo Her - co-chair
Shi-Ming Huang - co-chair
Files Date of Defense 2005-06-17 Date of Submission 2005-07-12