Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

By Shan Du, Mahmoud Ibrahim, Mohamed S. Shehata, Wael Badawy. IEEE transactions on circuits and systems for video technology (Print), 2013

820 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/fabf4efa0ce7837f24b91c617cf9954fee1df50f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ling-yu Duan
2. Jun Liu
3. Tao Mei
4. Wu Liu
5. Xinchen Liu
6. Huadong Ma
7. Xiaogang Wang
8. Luiz Oliveira

In Media Technology

1. Tao Mei
2. Wu Liu
3. Xinchen Liu
4. Huadong Ma
5. Jun Liu
6. Luiz Oliveira
7. Jonathan Petit
8. Shiqi Wang

Citations in the corpus

• VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild (2019, methodology): “The straightforward method is to identify vehicles by the recognition of license plates [10][4].”
• License Plate Detection and Recognition in Unconstrained Scenarios (2018, methodology): “LPs and characters), followed by handcrafted feature extraction methods and classical machine learning classifiers [1,4].”
• A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance (2016, background): “Besides, the license plate recognition is a complex multi-step process including plate detection, segmentation, shape adjustment, and character recognition as in [7,8].”
• AutoMan: a platform for integrating human-based and digital computation (2012, background): “Academic literature on the subject suggests that state-of-the-art systems can achieve accuracy near 90% under ideal conditions [11].”
• Pseudonym Schemes in Vehicular Networks: A Survey (2015, methodology): “Moreover, the introduction of Automatic License Plate Reader (ALPR) technology [32] is used to take digital pictures of vehicle license plates to recognize and record vehicle license plate numbers.”
• PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance (2018, background): “In real-world practice, parks and highways have adopted license plate recognition systems to identify vehicles [13], [14].”
• A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector (2018, methodology): “In addition, the following parameters were used for training the networks: 80k iterations (max batches) and learning rate = [1-3, 1-4, 1-5] with steps at 25k and 35k iterations.”