A two-stage character segmentation method for Chinese license plate. (August 2015)

• Record Type: Journal Article
• Authors: Tian, Jiangmin; Wang, Ran; Wang, Guoyou; Liu, Jianguo; Xia, Yuanchun
• Is Part Of: Computers & electrical engineering. Volume 46(2015)
• Page Start: 539
• Page End: 553
• Publication Date: 2015-08
• Subjects: License plate character segmentation -- Horizontal boundaries location -- Template matching -- A∗ path-finding; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.02.014
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: Characters on license plate with interferences are cut using a two-stage method. Plate frame is removed and vertical tilt is corrected before segmentation. A harrow-shaped filter with values of 0 and 1 is designed for matching. Boundaries of the remarkable space are located accurately in initial segmentation. A ∗ algorithm is used for the precise segmentation of connected characters. Abstract: As a part of character recognition, character segmentation (CS) plays an important role in automatic license plate recognition (ALPR) system. In recent years, lots of methods on CS have been proposed and they work well on their own datasets. However, it is still challenging to segment characters from images with frame, declination and quality degradation because of noises and overlapped, connected or fragmented characters. In this paper, we propose a two-stage segmentation method for Chinese license plate. At the first stage, a novel template matching method is presented using a harrow-shaped filter (HSF) bank and minimum response. It finds the locations of the segmenting points between characters roughly. Then, the accurate segmentations between connected or overlapped characters are adjusted by a variant of A ∗ path-finding algorithm at the second stage. Experiments on a challenging dataset including 2334 images demonstrate the effectiveness and efficiency of the proposed method.