TY - JOUR
T1 - Automatic License Plate Recognition
AU - Chang, Shyang Lih
AU - Chen, Li Shien
AU - Chung, Yun Chung
AU - Chen, Sei Wan
N1 - Funding Information:
Manuscript received December 11, 2002; revised December 8, 2003. This work was supported by the National Science Council, Republic of China, under Contract NSC-89-2218-E-003-002. The Associate Editor for this paper was A. Broggi.
PY - 2004/3
Y1 - 2004/3
N2 - Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.
AB - Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.
KW - Color edge detector
KW - Fuzzification
KW - License number identification
KW - License plate locating
KW - License plate recognition (LPR)
KW - Self-organizing (SO) character recognition
KW - Spring model
KW - Topological sorting
KW - Two-stage fuzzy aggregation
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U2 - 10.1109/TITS.2004.825086
DO - 10.1109/TITS.2004.825086
M3 - Article
AN - SCOPUS:2342525923
SN - 1524-9050
VL - 5
SP - 42
EP - 53
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 1
ER -