Open AccessCNN‐RNN based method for license plate recognition
Author(s) -
Shivakumara Palaiahnakote,
Tang Dongqi,
Asadzadehkaljahi Maryam,
Lu Tong,
Pal Umapada,
Hossein Anisi Mohammad
Publication year - 2018
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2018.1015
Subject(s) - computer science , discriminative model , convolutional neural network , artificial intelligence , pattern recognition (psychology) , license , context (archaeology) , feature extraction , feature (linguistics) , machine learning , speech recognition , operating system , paleontology , linguistics , philosophy , biology
Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle (e.g., cars) have numbers with dark background, while public vehicle (taxis/cabs) have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks namely, BLSTM (Bi‐Directional Long Short Term Memory), for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting (DCV), which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification.