Automatic license plate recognition

By Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, Sei-Wang Chen. 2004

908 citations recorded by the source corpus.

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%.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

License Plate Recognition From Still Images and Video Sequences: A Survey (2008, methodology): “In [20], self-organized neural networks based on Kohonen’s self-organized feature maps were implemented to handle noisy,”
An Algorithm for License Plate Recognition Applied to Intelligent Transportation System (2011, background): “Since the first line of characters is not processed, the information obtained by the existing LPR may not satisfy the practical application demand [1], [14], [17]–[19], [22], [23], [31], [35].”
A License Plate-Recognition Algorithm for Intelligent Transportation System Applications (2006, methodology): “Among the noncommercial systems that are presented in the literature, those portrayed in [4], [11], [12], [17], [18], [29], and [31] will be discussed in the following paragraphs.”
Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images (2017, neutral): “Privacy and Computer Vision Several works explore detecting individual privacy attributes such as license plates [53, 49, 6], age estimation from facial photographs [4], social relationships [45], face detection [40, 44], landmark detection [51] and occupation recognition [39].”
Automatic License Plate Recognition (ALPR): A State-of-the-Art Review (2013, background): “In [52], since only four colors (white, black, red, and green)”
Camera-based analysis of text and documents: a survey (2005, methodology): “Later on, Capel and Zisserman [8] show that Irani’s method is superior to the baseline unconstrained maximum likelihood (ML) estimator in noisy cases.”