The Study of Checking the Annual Inspection Status of Motorcycles Based on License Plate Recognition

• Main Authors: Chien-Hung Chen, 陳建宏
• Other Authors: Yo-Ping Huang
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/42222464205258491599

碩士 === 大同大學 === 資訊工程學系(所) === 96 === 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.