An empirical study of license plate detection and recognition algorithm for automatic traffic law enforcement | |
| Author | Dina, Nasa Zata |
| Call Number | AIT Thesis no.CS-14-04 |
| Subject(s) | Traffic regulations Pattern recognition systems |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. CS-14-04 |
| Abstract | The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about the vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to secure the entrance or exit way for vehi- cles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and character recognition. Recognition algorithms detect the position of a license plate and extract its characters. Vari- ous license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this thesis, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting license plates, the best result is obtained by a Haar cascade algorithm. After the best result on the license plate detection task is ob- tained, for segmentation, a Laplacian filter based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than template matching recognition. I summarize and analyze the overall performance of each method for comparison. |
| Year | 2014 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis : no. CS-14-04 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Dailey, Matthew N. |
| Examination Committee(s) | Phan Minh Dung;Chanathip Namprempre |
| Scholarship Donor(s) | Directorate General of Higher Education (DIKTI), Ministry of Education and Culture, Indonesia;Asian Institute of Technology Fellowship |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2014 |