Announcement for Downloading full text filePlease respect the Copyright Act.
All digital full text dissertation and theses from this website are authorized the copyright owners. These copyrighted full-text dissertation and theses can be only used for academic, research and non-commercial purposes. Users of this website can search, read, and print for personal usage. In respect of the Copyright Act of the Republic of China, please do not reproduce, distribute, change, or edit the content of these dissertations and theses without any permission. Please do not create any work based upon a pre-existing work by reproduction, Adaptation, Distribution or other means.
URN etd-0822108-211819 Statistics This thesis had been viewed 2816 times. Download 1107 times. Author Chien-Hung Chen Author's Email Address No Public. Department Computer Science and Enginerring Year 2007 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 92 Title The Study of Checking the Annual Inspection Status of Motorcycles Based on License Plate Recognition Keyword back propagation neural network License plate recognition character recovery horizontal and vertical projections search window feature matching feature matching search window horizontal and vertical projections character recovery License plate recognition back propagation neural network Abstract License plate recognition techniques have been successfully applied to the management of stolen cars, management of parking lots and traffic flow control. This study proposes a license plate based strategy for checking the annual inspection status of motorcycles from images taken along the roadside and at designated inspection stations.
Both a UMPC (Ultra Mobile Personal Computer) with a web camera and a desktop PC are used as the hardware platforms. In this study, the license plate recognition strategy consists of three main parts, including license plate location, segmentation of characters and characters recognition. The license plate locations in images are identified by means of integrated horizontal and vertical projections that are scanned using a search window. Moreover, a character recovery method and a plate-region filter are exploited to enhance the success rate and the tilt license plate will be adjusted. The segmentation of characters uses the feature of license plate of characters to segment each one. Besides, the type of license plates can also be defined in this procedure. Character recognition is achieved using both a back-propagation artificial neural network and feature matching. The identified license plate can then be compared with entries in a database to check the inspection status of the motorcycle. Experiments yield a recognition rate of 95.7% and 93.9% based on test images from roadside and inspection stations, respectively. It takes less than 1 second on a UMPC (Celeron 900MHz with 256MB memory) and about 293 milliseconds on a PC (Intel Pentium 4 3.0GHz with 1GB memory) to correctly recognize a license plate. Challenges associated with recognizing license plates from roadside and designated inspection stations images are also discussed.
Advisor Committee Shang-Lin Hsieh - advisor
Yo-Ping Huang - advisor
Leehter Yao - co-chair
Files Date of Defense 2008-06-13 Date of Submission 2008-08-26