{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T04:31:54Z","timestamp":1788928314177,"version":"build-2803163510"},"reference-count":25,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T00:00:00Z","timestamp":1587081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002628","name":"Incheon National University","doi-asserted-by":"publisher","award":["2014"],"award-info":[{"award-number":["2014"]}],"id":[{"id":"10.13039\/501100002628","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The orientation of plate images in license plate recognition is one of the factors that influence its accuracy. In particular, tilted plate images are harder to detect and recognize characters with than aligned ones. To this end, the rectification of plates in a preprocessing step is essential to improve their performance. We propose deep models to estimate four-corner coordinates of tilted plates. Since the predicted corners can then be used to rectify plate images, they can help improve plate recognition in plate recognition. The main contributions of this work are a set of open-structured hybrid networks to predict corner positions and a novel loss function that combines pixel-wise differences with position-wise errors, producing performance improvements. Regarding experiments using proprietary plate images, one of the proposed modes produces a 3.1% improvement over the established warping method.<\/jats:p>","DOI":"10.3390\/info11040221","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T03:23:06Z","timestamp":1587439386000},"page":"221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Homography for License Plate Detection"],"prefix":"10.3390","volume":"11","author":[{"given":"Hojin","family":"Yoo","sequence":"first","affiliation":[{"name":"Dept. of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyungkoo","family":"Jun","sequence":"additional","affiliation":[{"name":"Dept. of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tian, Z., Huang, W., He, T., He, P., and Qiao, Y. (2016, January 11\u201314). Detecting text in natural image with connectionist text proposal network. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_4"},{"key":"ref_2","unstructured":"Li, H., and Shen, C. (2016). Reading car license plates using deep convolutional neural networks and lstms. arXiv."},{"key":"ref_3","unstructured":"Masood, S.Z., Shu, G., Dehghan, A., and Ortiz, E.G. (2017). License plate detection and recognition using deeply learned convolutional neural networks. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Naimi, A., Kessentini, Y., and Hammami, M. (2016, January 16\u201321). Multi-nation and multi-norm license plates detection in real traffic surveillance environment using deep learning. Proceedings of the International Conference on Neural Information Processing, Kyoto, Japan.","DOI":"10.1007\/978-3-319-46672-9_52"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6429","DOI":"10.1007\/s00500-017-2696-2","article-title":"Vehicle license plate detection using region-based convolutional neural networks","volume":"22","author":"Rafique","year":"2018","journal-title":"Soft Comput."},{"key":"ref_6","unstructured":"Girshick, R. (2015). Fast R-CNN object detection with Caffe. Microsoft Res., Available online: https:\/\/tutorial.caffe.berkeleyvision.org\/caffe-cvpr15-detection.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TITS.2017.2784093","article-title":"A new CNN-based method for multi-directional car license plate detection","volume":"19","author":"Xie","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Hou, S., Wang, Z., and Zha, Z. (2018, January 8\u201314). Towards human-level license plate recognition. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01219-9_19"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, G.R., Diniz, M.A., Laroca, R., Menotti, D., and Schwartz, W.R. (2019). Multi-task learning for low-resolution license plate recognition. Iberoamerican Congress on Pattern Recognition, Havana, Cuba, 28\u201331 October 2019, Springer.","DOI":"10.1007\/978-3-030-33904-3_23"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lee, Y., Jun, J., Hong, Y., and Jeon, M. (2019). Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution. arXiv.","DOI":"10.5220\/0007378300680076"},{"key":"ref_12","unstructured":"Montazzolli Silva, S., and Rosito Jung, C. (2018, January 8\u201314). License plate detection and recognition in unconstrained scenarios. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, K.K., Kim, K.I., Kim, J.B., and Kim, H.J. (2000). Learning-based approach for license plate recognition. Neural Networks for Signal Processing X, Proceedings of the IEEE Signal Processing Society Workshop (Cat. No. 00TH8501), Sydney, Australia, 11\u201313 December 2000, IEEE.","DOI":"10.1109\/NNSP.2000.890140"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/TITS.2008.922938","article-title":"License plate recognition from still images and video sequences: A survey","volume":"9","author":"Anagnostopoulos","year":"2008","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/TVT.2012.2226218","article-title":"Application-oriented license plate recognition","volume":"62","author":"Hsu","year":"2012","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"023020","DOI":"10.1117\/1.JEI.24.2.023020","article-title":"End-to-end system of license plate localization and recognition","volume":"24","author":"Zhu","year":"2015","journal-title":"J. Electron. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"146","DOI":"10.3103\/S0146411618020104","article-title":"FCN and LSTM based computer vision system from recognition of vehicle type, license plate number, and registration country","volume":"52.2","author":"Dorbe","year":"2018","journal-title":"Autom. Control Compu. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/TITS.2016.2639020","article-title":"Segmentation-and annotation-free license plate recognition with deep localization and failure identification","volume":"18","author":"Bulan","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lee, Y., Lee, J., Ahn, H., and Jeon, M. (2019, January 27\u201328). SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition. Proceedings of the IEEE International Conference on Computer Vision Workshops, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00131"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q.V. (2019, January 16\u201320). Mnasnet: Platform-aware neural architecture search for mobile. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00293"},{"key":"ref_23","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_24","unstructured":"(2020, February 01). Mobilenet V2. Available online: https:\/\/github.com\/pytorch\/vision\/blob\/master\/torchvision\/models\/mobilenet.py."},{"key":"ref_25","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An imperative style, high-performance deep learning library. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/221\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:31:26Z","timestamp":1760362286000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,17]]},"references-count":25,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["info11040221"],"URL":"https:\/\/doi.org\/10.3390\/info11040221","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,17]]}}}