{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T05:11:19Z","timestamp":1777957879120,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T00:00:00Z","timestamp":1637280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MSIT (Ministry of Science &amp; ICT), Korea, under the ITRC support program.","award":["IITP-2021-2017-0-01633"],"award-info":[{"award-number":["IITP-2021-2017-0-01633"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.<\/jats:p>","DOI":"10.3390\/s21227696","type":"journal-article","created":{"date-parts":[[2021,11,21]],"date-time":"2021-11-21T21:00:50Z","timestamp":1637528450000},"page":"7696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Deep Learning Based Approach for Localization and Recognition of Pakistani Vehicle License Plates"],"prefix":"10.3390","volume":"21","author":[{"given":"Umair","family":"Yousaf","sequence":"first","affiliation":[{"name":"Department of Software Engineering, University of Sialkot, Sialkot 51040, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6955-8876","authenticated-orcid":false,"given":"Ahmad","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22044, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3058-5794","authenticated-orcid":false,"given":"Hazrat","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22044, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fiaz Gul","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22044, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5836-0488","authenticated-orcid":false,"given":"Zia ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22044, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6334-4773","authenticated-orcid":false,"given":"Sajid","family":"Shah","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22044, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9420-1588","authenticated-orcid":false,"given":"Farman","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Software, Sejong University, Seoul 05006, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1085-1568","authenticated-orcid":false,"given":"Sangheon","family":"Pack","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea University, Seoul 02841, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1208-8655","authenticated-orcid":false,"given":"Safdar","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Lahore, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,19]]},"reference":[{"key":"ref_1","unstructured":"Sakthivel, N., and Swamydoss, D. (2017). An Optimized Algorithm for Car Plate Recognition Using Artificial Neural Network for a Mobile Application without Segmentation. Asian J. Appl. Sci., 5, Available online: https:\/\/www.ajouronline.com\/index.php\/AJAS\/article\/view\/4645."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Patel, C., Shah, D., and Patel, A. (2013). Automatic number plate recognition system (anpr): A survey. Int. J. Comput. Appl., 69.","DOI":"10.5120\/11871-7665"},{"key":"ref_3","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016, January 5\u201310). R-FCN: Object detection via region-based fully convolutional networks. Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"21771","DOI":"10.1007\/s11042-021-10510-1","article-title":"A novel weight initialization with adaptive hyper-parameters for deep semantic segmentation","volume":"80","author":"Haq","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_8","unstructured":"Haq, N.U., Ur Rehman, Z., Khan, A., Din, A., Shah, S., Ullah, A., and Qayum, F. (2020). Impact of data smoothing on semantic segmentation. Neural Comput. Appl., 1\u201310."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nam, H., and Han, B. (2016, January 27\u201330). Learning multi-domain convolutional neural networks for visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.465"},{"key":"ref_10","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H. (, January 11\u201314). Fully-convolutional siamese networks for object tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_12","unstructured":"Kendall, A., Badrinarayanan, V., and Cipolla, R. (2015). Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Ciregan, D., Meier, U., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for image classification. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1007\/s10588-018-9271-y","article-title":"Ligature categorization based Nastaliq Urdu recognition using deep neural networks","volume":"25","author":"Rafeeq","year":"2019","journal-title":"Comput. Math. Organ. Theory"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"61408","DOI":"10.1109\/ACCESS.2021.3074422","article-title":"Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"150530","DOI":"10.1109\/ACCESS.2019.2947484","article-title":"A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection","volume":"7","author":"Qummar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Huang, W., Qiao, Y., and Tang, X. (2014, January 6\u201312). Robust scene text detection with convolution neural network induced mser trees. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_33"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a deep convolutional network for image super-resolution. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_20","unstructured":"Malik, S.M., and Hafiz, R. (2014, January 30\u201331). Automatic Number Plate Recognition based on connected component analysis technique. Proceedings of the 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET\u20192014), London, UK."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1504\/IJCVR.2016.073761","article-title":"Automatic number plate recognition system by character position method","volume":"6","author":"Singh","year":"2016","journal-title":"Int. J. Comput. Vis. Robot."},{"key":"ref_22","unstructured":"Khan, J.A., and Shah, M.A. (2016, January 7\u20138). Car Number Plate Recognition (CNPR) system using multiple template matching. Proceedings of the 2016 22nd International Conference on Automation and Computing (ICAC), Colchester, UK."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Khan, J.A., Shah, M.A., Wahid, A., Khan, M.H., and Shahid, M.B. (2017, January 19\u201321). Enhanced car number plate recognition (ECNPR) system by improving efficiency in preprocessing steps. Proceedings of the 2017 International Conference on Communication Technologies (ComTech), Rawalpindi, Pakistan.","DOI":"10.1109\/COMTECH.2017.8065766"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Soomro, S.R., Javed, M.A., and Memon, F.A. (2012, January 22\u201323). Vehicle number recognition system for automatic toll tax collection. Proceedings of the 2012 International Conference of Robotics and Artificial Intelligence, Rawalpindi, Pakistan.","DOI":"10.1109\/ICRAI.2012.6413377"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Haider, S.A., and Khurshid, K. (2017, January 5\u20137). An implementable system for detection and recognition of license plates in Pakistan. Proceedings of the 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT), Karachi, Pakistan.","DOI":"10.1109\/ICIEECT.2017.7916553"},{"key":"ref_26","unstructured":"Rasheed, S., Naeem, A., and Ishaq, O. (2012, January 24\u201326). Automated number plate recognition using hough lines and template matching. Proceedings of the World Congress on Engineering and Computer Science, San Francisco, CA, USA."},{"key":"ref_27","first-page":"244","article-title":"Localization of license plate number using dynamic image processing techniques and genetic algorithms","volume":"18","author":"Samra","year":"2013","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/TITS.2015.2496545","article-title":"Vehicle license plate recognition based on extremal regions and restricted Boltzmann machines","volume":"17","author":"Gou","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bhutta, M.U.M., Mahmood, H., and Malik, H. (2014, January 7\u20139). An intelligent approach for robust detection and recognition of multiple color and font styles automobiles license plates: A feature-based algorithm. Proceedings of the 2014 International Conference on Audio, Language and Image Processing, Shanghai, China.","DOI":"10.1109\/ICALIP.2014.7009936"},{"key":"ref_30","unstructured":"Li, H., and Shen, C. (2016). Reading car license plates using deep convolutional neural networks and lstms. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Selmi, Z., Halima, M.B., and Alimi, A.M. (2017, January 9\u201315). Deep learning system for automatic license plate detection and recognition. Proceedings of the 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan.","DOI":"10.1109\/ICDAR.2017.187"},{"key":"ref_32","unstructured":"Cheang, T.K., Chong, Y.S., and Tay, Y.H. (2017). Segmentation-free vehicle license plate recognition using ConvNet-RNN. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"033001","DOI":"10.1117\/1.JEI.24.3.033001","article-title":"Vehicle license plate recognition using visual attention model and deep learning","volume":"24","author":"Zang","year":"2015","journal-title":"J. Electr. Imaging"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jain, V., Sasindran, Z., Rajagopal, A., Biswas, S., Bharadwaj, H.S., and Ramakrishnan, K. (2016, January 18\u201322). Deep automatic license plate recognition system. Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, Guwahati, Assam, India.","DOI":"10.1145\/3009977.3010052"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Laroca, R., Severo, E., Zanlorensi, L.A., Oliveira, L.S., Gon\u00e7alves, G.R., Schwartz, W.R., and Menotti, D. (2018, January 8\u201313). A robust real-time automatic license plate recognition based on the YOLO detector. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489629"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1007\/s00500-017-2503-0","article-title":"Convolutional neural networks-based intelligent recognition of Chinese license plates","volume":"22","author":"Liu","year":"2018","journal-title":"Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Hou, S., Wang, Z., and Zha, Z.J. (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_38","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.patcog.2019.01.020","article-title":"Moran: A multi-object rectified attention network for scene text recognition","volume":"90","author":"Luo","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1109\/TPAMI.2018.2848939","article-title":"Aster: An attentional scene text recognizer with flexible rectification","volume":"41","author":"Shi","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"Silvano, G., Endo, P.T., Ribeiro, V.C.T., Greati, V., Silva, I., Lynn, T., and Bezerra, A. (2021, January 16). Artificial Mercosur License Plates, V2. Available online: https:\/\/data.mendeley.com\/datasets\/nx9xbs4rgx\/2."},{"key":"ref_42","unstructured":"Roboflow (2021, January 16). License Plates Dataset. Available online: https:\/\/public.roboflow.com\/object-detection\/license-plates-us-eu."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Usmankhujaev, S., Lee, S., and Kwon, J. (2019, January 26\u201328). Korean license plate recognition system using combined neural networks. Proceedings of the International Symposium on Distributed Computing and Artificial Intelligence, Avila, Spain.","DOI":"10.1007\/978-3-030-23887-2_2"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7696\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:32:57Z","timestamp":1760167977000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,19]]},"references-count":43,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227696"],"URL":"https:\/\/doi.org\/10.3390\/s21227696","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,19]]}}}