This paper presents a vehicle license plate recognition method based on character-specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs). First, coarse license plate detection (LPD) is performed by top-hat transformation, vertical edge detection, morphological operations, and various validations. Then, character-specific ERs are extracted as character regions in license plate candidates. Followed by suitable selection of ERs, the segmentation of characters and coarse-to-fine LPD are achieved simultaneously. Finally, an offline trained pattern classifier of HDRBM is applied to recognize the characters. The proposed method is robust to illumination changes and weather conditions during 24 h or one day. Experimental results on thorough data sets are reported to demonstrate the effectiveness of the proposed approach in complex traffic environments.
Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines
IEEE Transactions on Intelligent Transportation Systems ; 17 , 4 ; 1096-1107
2016-04-01
3061949 byte
Article (Journal)
Electronic Resource
English
Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines
Online Contents | 2016
|Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines
Online Contents | 2015
|European Patent Office | 2015
|Vehicle License Plate Registration Recognition System
Trans Tech Publications | 2013
|