Abstract
Automatic License Plate Recognition (ALPR) is a technology that recognizes license plates from a given image and then uses optical character recognition on it to read the registration plates. We propose a two-staged approach to accomplish this. In the first stage, we use transfer learning with state-of-the-art YOLO object detector. This gives an efficient algorithm to detect the exact location of the license plates in the given image. We compare two versions of YOLO (YOLOv2 and YOLOv3) to gain insight into the differences between the versions. Then, in the second stage, we use image enhancement techniques to run optical character recognition to obtain the final output text. The system is trained on a custom dataset, in which we have manually annotated all the images. We use Intersection over Union (IoU) and mean Average Precision (mAP) as our evaluation metrics and obtain an IoU of 58.24% and mAP of 53.30% for YOLOv3 and 57.91% IoU and 51.28% mAP for YOLOv2.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lee D, Yoon S, Lee J, Park DS (2016) Real-time license plate detection based on faster R-CNN. KIPS Trans Softw Data Eng 5:511–520. https://doi.org/10.3745/ktsde.2016.5.11.511
Usmankhujaev S, Lee S, Kwon J (2020) Korean license plate recognition system using combined neural networks. In: Herrera F, Matsui K, Rodríguez-González S (eds) Distributed computing and artificial intelligence, 16th international conference. DCAI 2019. Advances in intelligent systems and computing, vol 1003. Springer, Cham
Masood SZ, Shu G, Dehghan A, Ortiz EG (2017) License plate detection and recognition using deeply learned convolutional neural networks
Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? 3320–3328
Han J, Yao J, Zhao J, Liu Y (2019) Multi-oriented and scale-invariant license plate detection based on convolutional neural networks. Sensors 19:1175. https://doi.org/10.3390/s19051175
Girshick R (2015) Fast R-CNN. In: 2015 IEEE international conference on computer vision (ICCV). Santiago, pp 1440–1448
Ren S, He K, Girshick R Sun J (2017, June) Faster R-CNN: towards real-time object detection with region proposal networks. In: IEEE transactions on pattern analysis and machine intelligence, vol 39, no. 6. pp 1137–1149
Tian J, Wang G, Liu J (20, March 20) Semantic region proposals for adaptive license plate detection in open environment. J Electron Imaging 28(2):023017
Tian J, Wang G, Liu J (2019, March 9) Semantic region proposals for adaptive license plate detection in open environment. J Electron Imaging 28(2):023017
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). Las Vegas, NV, pp 779–788
Redmon Joseph, Farhadi Ali (2016) YOLO9000: Better. Faster, Stronger
Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement
Hamad KA, Kaya M (2016) A detailed analysis of optical character recognition technology. Int J Appl Mathe Electron Comput 4:244–244. https://doi.org/10.18100/ijamec.270374
Smith R (2007) An overview of the tesseract OCR engine. In: Ninth international conference on document analysis and recognition (ICDAR 2007), Parana, pp 629–633
Izidio DM, Ferreira AP, Medeiros HR, Barros EN (2018) An embedded automatic license plate recognition system using deep learning. In: 2018 VIII Brazilian symposium on computing systems engineering (SBESC). Salvador, Brazil, pp 38–45
Abdullah S, Hasan MM, Islam SM (2018) YOLO-based three-stage network for Bangla license plate recognition in Dhaka Metropolitan City. In: 2018 international conference on Bangla speech and language processing (ICBSLP). Sylhet, pp 1–6
Silva SM, Jung CR (2017). Real-Time Brazilian license plate detection and recognition using deep convolutional neural networks. https://doi.org/10.1109/sibgrapi.2017.14
https://www.kaggle.com/dataturks/vehicle-number-plate-detection
Sun H, Fu M, Abdussalam A, Huang Z, Sun S, Wang W (2019) License plate detection and recognition based on the YOLO detector and CRNN-12. In: Sun S (eds) Signal and information processing, networking and computers. ICSINC 2018. Lecture notes in electrical engineering, vol 494. Springer, Singapore
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gandhi, J., Jain, P., Kurup, L. (2020). YOLO Based Recognition of Indian License Plates. In: Vasudevan, H., Michalas, A., Shekokar, N., Narvekar, M. (eds) Advanced Computing Technologies and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3242-9_39
Download citation
DOI: https://doi.org/10.1007/978-981-15-3242-9_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3241-2
Online ISBN: 978-981-15-3242-9
eBook Packages: EngineeringEngineering (R0)