License Plate Recognition From Still Images and Video Sequences: A Survey Export

@article{citeulike:4375757, abstract = {<para> License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment. </para>}, author = {Anagnostopoulos, C. N. E. and Anagnostopoulos, I. E. and Psoroulas, I. D. and Loumos, V. and Kayafas, E.}, booktitle = {Intelligent Transportation Systems, IEEE Transactions on}, citeulike-article-id = {4375757}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TITS.2008.922938}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4518951}, doi = {10.1109/TITS.2008.922938}, journal = {Intelligent Transportation Systems, IEEE Transactions on}, keywords = {ocr}, number = {3}, pages = {377--391}, posted-at = {2009-04-22 16:58:12}, priority = {2}, title = {License Plate Recognition From Still Images and Video Sequences: A Survey}, url = {http://dx.doi.org/10.1109/TITS.2008.922938}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:4375757 L3 - citeulike-article-id:4375757 N2 - <para> License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment. </para> IS - 3 JF - Intelligent Transportation Systems, IEEE Transactions on EP - 391 TI - License Plate Recognition From Still Images and Video Sequences: A Survey VL - 9 T2 - Intelligent Transportation Systems, IEEE Transactions on SP - 377 KW - ocr AU - Anagnostopoulos, CNE AU - Anagnostopoulos, IE AU - Psoroulas, ID AU - Loumos, V AU - Kayafas, E PY - 2008/// UR - http://dx.doi.org/10.1109/TITS.2008.922938 ER -