Abstract
Criminal investigations regularly involve the deciphering of license plates (LPs) of vehicles. Unfortunately, the image or video source material typically stems from uncontrolled sources, and may be subject to severe degradations such as extremely low resolution, strong compression, low contrast or over- resp. underexposure. While LP recognition has a long history in computer vision research, the deciphering under such severe degradations is still an open issue. Moreover, since the data source is not controlled, it cannot be assumed that the exact form of degradation is covered in the training set.
In this work, we propose using convolutional recurrent neural networks (CRNN) for the recognition of LPs from images with strong unseen degradations. The CRNN clearly outperforms an existing conventional CNN in this scenario. It also provides an additional particular advantage for criminal investigations, namely to create top-n sequence predictions. Even a low number of top-n candidates improves the recognition performance considerably.
We gratefully acknowledge support by the German Federal Ministry of Education and Research (BMBF) under Grant No. 13N15319.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdullah, S., Hasan, M.M., Islam, S.M.S.: YOLO-based three-stage network for Bangla license plate recognition in Dhaka metropolitan city. In: IEEE International Conference on Bangla Speech and Language Processing, pp. 1–6 (2018)
Agarwal, S., Tran, D., Torresani, L., Farid, H.: Deciphering Severely Degraded License Plates. Electronic Imaging 2017(7), 138–143 (2017)
Bovik, A.C.: Handbook of Image and Video Processing. Academic Press, Cambridge (2010)
Council of EU: Collection of Laws of the Czech Republic. No. 343/2014 (2014)
Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: 23rd International Conference on Machine Learning, pp. 369–376 (2006)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. In: IEEE International Joint Conference on Neural Networks, vol. 4, pp. 2047–2052 (2005)
Kaiser, P., Schirrmacher, F., Lorch, B., Riess, C.: Learning to decipher license plates in severely degraded images. In: Del Bimbo, A., et al. (eds.) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol. 12666. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68780-9_43
Laroca, R., Menotti, D.: Automatic license plate recognition: an efficient and layout-independent system based on the YOLO detector. In: Anais Estendidos do XXXIII Conference on Graphics, Patterns and Images, pp. 15–21. SBC (2020)
Laroca, R., et al.: A robust real-time automatic license plate recognition based on the YOLO detector. In: IEEE International Joint Conference on Neural Networks, pp. 1–10 (2018)
Li, H., Wang, P., Shen, C.: Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks. IEEE Transactions on Intelligent Transportation Systems 20(3), 1126–1136 (2018)
Lorch, B., Agarwal, S., Farid, H.: Forensic reconstruction of severely degraded license plates. Electron. Imaging 2019(5), 529-1 (2019)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Riaz, W., Azeem, A., Chenqiang, G., Yuxi, Z., Khalid, W., et al.: YOLO based recognition method for automatic license plate recognition. In: IEEE International Conference on Advances in Electrical Engineering and Computer Applications, pp. 87–90 (2020)
Rossi, G., Fontani, M., Milani, S.: Neural network for denoising and reading degraded license plates. In: Del Bimbo, A., et al. (eds.) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol. 12666. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68780-9_39
Shi, B., Bai, X., Yao, C.: An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(11), 2298–2304 (2016)
Shivakumara, P., Tang, D., Asadzadehkaljahi, M., Lu, T., Pal, U., Anisi, M.H.: CNN-RNN based method for license plate recognition. CAAI Transactions on Intelligence Technology 3(3), 169–175 (2018)
Špaňhel, J., Sochor, J., Juránek, R., Herout, A., Maršík, L., Zemčík, P.: Holistic recognition of low quality license plates by CNN using track annotated data. In: 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 1–6 (2017)
Suvarnam, B., Ch, V.S.: Combination of CNN-GRU model to recognize characters of a license plate number without segmentation. In: 5th International Conference on Advanced Computing & Communication Systems, pp. 317–322 (2019)
Tourani, A., Shahbahrami, A., Soroori, S., Khazaee, S., Suen, C.Y.: A Robust Deep Learning Approach for Automatic Iranian Vehicle License Plate Detection and Recognition for Surveillance Systems. IEEE Access 8, 201317–201330 (2020)
Zhang, H., Sun, F., Zhang, X., Zheng, L.: License plate recognition model based on CNN+LSTM+CTC. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds.) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol. 1059. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-0121-0_52
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Moussa, D., Maier, A., Schirrmacher, F., Riess, C. (2021). Sequence-Based Recognition of License Plates with Severe Out-of-Distribution Degradations. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-89131-2_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89130-5
Online ISBN: 978-3-030-89131-2
eBook Packages: Computer ScienceComputer Science (R0)