Licence plates and traffic signs detection and recognition have a number of different applications relevant for transportation systems, such as traffic monitoring, detection of stolen vehicles, driver navigation support or any statistical research. A number of methods have been proposed, but only for particular cases and working under constraints (e.g. known text direction or high resolution). Therefore a new class of locally threshold separable detectors based on extremal regions, which can be adapted by machine learning techniques to arbitrary shapes, is proposed. In the test set of licence plate images taken from different viewpoints (-45/spl deg/,45/spl deg/), scales (from seven to hundreds of pixels height) even in bad illumination conditions and partial occlusions, the high detection accuracy is achieved (95%). Finally we present the detector generic abilities by traffic signs detection. The standard classifier (neural network) within the detector selects a relevant subset of extremal regions, i.e. regions that are connected components of a thresholded image. Properties of extremal regions render the detector very robust to illumination change and partial occlusions. Robustness to a viewpoint change is achieved by using invariant descriptors and/or by modelling shape variations by the classifier. The time-complexity of the detection is approximately linear in the number of pixel and a non-optimized implementation runs at about 1 frame per second for a 640 /spl times/ 480 image on a high-end PC.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Unconstrained licence plate and text localization and recognition


    Contributors:
    Matas, J. (author) / Zimmermann, K. (author)


    Publication date :

    2005-01-01


    Size :

    621325 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Unconstrained licence plate and text localization and recognition

    Matas, J. / Zimmermann, K. | Tema Archive | 2005


    An Unconstrained Licence Plate and Text Localization and Recognition

    Zimmermann, K. / Matas, G. / IEEE | British Library Conference Proceedings | 2005


    Licence plate

    FEI ZHENGHUA | European Patent Office | 2016

    Free access

    SMART LICENCE PLATE

    ATTIA MOHAMED DWEDAR AHMED | European Patent Office | 2017

    Free access

    LICENCE PLATE SUPPORT STRUCTURE

    INAGAKI TOSHIYA | European Patent Office | 2017

    Free access