The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks
Abstract
In this paper, we tackle the problem of car license plate detection and recognition in natural scene images. We propose a unified deep neural network, which can localize license plates and recognize the letters simultaneously in a single forward pass. The whole network can be trained end-to-end. In contrast to existing approaches which take license plate detection and recognition as two separate tasks and settle them step by step, our method jointly solves these two tasks by a single network. It not only avoids intermediate error accumulation but also accelerates the processing speed. For performance evaluation, four data sets including images captured from various scenes under different conditions are tested. Extensive experiments show the effectiveness and the efficiency of our proposed approach.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
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YOLO9000: Better, Faster, Stronger
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Shi, B., Bai, X., & Yao, C. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2646371 · Cited by 3,172
A Novel Connectionist System for Unconstrained Handwriting Recognition
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ICDAR 2015 competition on Robust Reading
Karatzas, D., Gomez-Bigorda, L., Nicolaou, A., Ghosh, S., Bagdanov, A., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V. R., Lu, S., et al. (2015) · 2015 13th International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2015.7333942 · Cited by 1,547
Cited by
How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
Ye, J., Zhao, J., Ye, K., & Xu, C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3043250 · Cited by 281
Metadata
- Authors
- Li, H., Wang, P., & Shen, C.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2019-03
- Volume
- 20
- Issue
- 3
- Pages
- 1126-1136
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2962829835
- Cited by
- 346
- References
- 37
- FWCI
- 28.20
- Type
- journal article
- Language
- en
- Open access
- closed
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