SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218) 1998
DOI: 10.1109/icsmc.1998.727533
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A high performance license plate recognition system

Abstract: This paper presents a powerful automated license plate recognition system, which is able to read license numbers of cars, even under circumstances, which are far from ideal. In a real-life test, the percentage of rejected plates was 13%, whereas 0.4% of the plates were misclassified. Suggestions for further improvements are given.

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Cited by 91 publications
(54 citation statements)
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“…A leakage term , which dissipates activations of SO neurons once a stimulus has been removed, is introduced for every SO neuron. The net input to then sums the inputs from the stimulus, lateral interaction, and leakage (11) During competition among SO neurons, the winner is determined by . Next, the winner together with its neighbors, say set , engage in a group learning process.…”
Section: ) Template Testmentioning
confidence: 99%
See 1 more Smart Citation
“…A leakage term , which dissipates activations of SO neurons once a stimulus has been removed, is introduced for every SO neuron. The net input to then sums the inputs from the stimulus, lateral interaction, and leakage (11) During competition among SO neurons, the winner is determined by . Next, the winner together with its neighbors, say set , engage in a group learning process.…”
Section: ) Template Testmentioning
confidence: 99%
“…There are two major tasks involved in the identification stage, character separation and character recognition. Character separation has in the past been accomplished by such techniques as projection [11], [30], morphology [2], [10], [28] relaxation labeling, connected components [25], and blob coloring. Every technique has its own advantages and disadvantages.…”
Section: Introductionmentioning
confidence: 99%
“…In previous researches, there are numerous methods such as simple Euclidean distance [15], Hidden Markov Model (HMM) [13], Artificial Neural Network (ANN) [12,16,17], Support Vector Machine (SVM) [18] and template matching [7,19]. Table 1 shows the various algorithms for OCR along with its strengths and weaknesses.…”
Section: Optical Character Recognitionmentioning
confidence: 99%
“…The Tesseract OCR library [20] was used as it can be integrated into Android SDK as well as it can provide two methods, including ANN and template matching. Need huge and representative training set [15] Markov model (HMM)…”
Section: Optical Character Recognitionmentioning
confidence: 99%
“…Some of uncertain factors unfortunately existed such as bad weather, false license plat and night time recognition etc that result in undesirable recognition failure event happened. By using this analysis method, the Harbor Security Center can make a proper decision for the automatic system operation [6,11]. Manual gate)…”
Section: Applied Examplementioning
confidence: 99%