Open AccessEfficient and unified license plate recognition via lightweight deep neural network
Author(s) -
Qin Shuxin,
Liu Sijiang
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.1130
Subject(s) - computer science , license , convolutional neural network , artificial intelligence , segmentation , pattern recognition (psychology) , line (geometry) , deep learning , optical character recognition , artificial neural network , connectionism , task (project management) , image segmentation , backpropagation , machine learning , image (mathematics) , engineering , operating system , geometry , mathematics , systems engineering
In this study, the authors are interested in building a unified deep learning framework to solve the recognition problem of both single‐line and double‐line car license plates. Most existing methods are designed for single‐line license plate recognition. For double‐line cases, detection and segmentation are usually adopted firstly to locate each line of characters. These methods are usually environmentally sensitive and will bring propagation of error between segmentation and recognition. To solve the problems, the authors propose a unified method that can recognise both single‐line and double‐line license plates in an end‐to‐end way without line segmentation and character segmentation. Specifically, an improved lightweight convolutional neural network is used to extract features efficiently. Then, the multi‐task learning strategy is used to simultaneously perform license plate classification and character recognition. Finally, recognition task is treated as sequence labelling problems, which are solved by connectionist temporal classification directly. Experimental results indicate that the proposed method significantly outperforms the previous state‐of‐the‐art methods on both public datasets and the synthetic dataset.