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Vietnam Vehicle Number Recognition Based on an Improved CRNN with Attention Mechanism

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International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Applications for automatic license plate recognition (ALPR) are evaluated as an important technology for effective traffic control. In particular, the most used application today for license plate recognition is at highways, toll stations, agencies, parking, and schools, with the support of camera equipment that provides high accuracy. In this study, Convolutional Recurrent Neural Networks (CRNNs) are utilized for the license plate recognition task. We are develo** an automatic license plate recognition system designed for Vietnamese license plates, intended for use in indoor parking facilities with fixed cameras. In this context, the problem presents three main steps: (1) using the YOLO model to detect vehicles in the given images, (2) using the WPOD-NET model to extract license plates, and (3) introducing a new method based on an improved Convolution Recurrent Neural Network (CRNN) with combination between connectionist temporal classification (CTC) and attention mechanism to recognize characters on license plates. Our CRNN model is jointly trained with both CTC and attention objective functions. Experimental results on a license plate database collected from an indoor parking area achieved a Word Error Rate (WER) of 0.014 in the optical character recognition (OCR) task. The experimental results demonstrate that the proposed model performs well for vehicle number plate recognition and can be applied to real-time applications.

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Abbreviations

ALPR :

Automatic license plate recognition

BLSTM :

Bidirectional Long short-term memory

CNN :

Convolutional neural network

CRNN :

Convolutional recurrent neural network

CTC :

Connectionist temporal classification

FAN :

Focusing attention network

HOG :

Histogram of oriented gradients

IoU :

Intersection over union

KNN :

K-nearest neighbors

LBP :

Local binary pattern

LSTM :

Long short-term memory

OCR :

Optical character recognition

SGD :

Stochastic gradient descent

STN :

Spatial transformer networks

SVM :

Support vector machines

WER :

Word error rate

WPOD-Net :

Warped planar object detection network

YOLO :

You only look once

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Acknowledgements

This work was partly supported by Saigon University.

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Authors and Affiliations

  1. Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam

    Le Tran Anh Dang, Vong Duong Ngoc, Pham Cung Le Thien Vu, Nguyen Nhat Truong, Pham The Bao & Tan Dat Trinh

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  1. Le Tran Anh Dang
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  2. Vong Duong Ngoc
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  3. Pham Cung Le Thien Vu
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  4. Nguyen Nhat Truong
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  5. Pham The Bao
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Correspondence to Tan Dat Trinh.

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Dang, L.T.A., Ngoc, V.D., Thien Vu, P.C.L. et al. Vietnam Vehicle Number Recognition Based on an Improved CRNN with Attention Mechanism. Int. J. ITS Res. (2024). https://doi.org/10.1007/s13177-024-00402-7

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  • DOI: https://doi.org/10.1007/s13177-024-00402-7

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