{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:17:19Z","timestamp":1767651439686,"version":"3.37.3"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:00:00Z","timestamp":1628553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:00:00Z","timestamp":1628553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National\u00a0vocational\u00a0education\u00a0teachers\u00a0innovation\u00a0team\u00a0project\u00a0of\u00a0China","award":["YB2020020103"],"award-info":[{"award-number":["YB2020020103"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61701267","61971251"],"award-info":[{"award-number":["61701267","61971251"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Postdoctoral Foundation of Chin","award":["2019M663474"],"award-info":[{"award-number":["2019M663474"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s11760-021-01997-0","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T18:02:27Z","timestamp":1628618547000},"page":"543-550","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Character classification algorithm based on the low-dimensional feature-optimized model"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0623-2825","authenticated-orcid":false,"given":"Kun","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Qianqian","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Cuncun","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Yafeng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Haiyan","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Jiawen","family":"Jian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,10]]},"reference":[{"issue":"9","key":"1997_CR1","doi-asserted-by":"publisher","first-page":"3686","DOI":"10.1109\/TITS.2019.2931791","volume":"21","author":"S-L Chen","year":"2019","unstructured":"Chen, S.-L., et al.: Simultaneous end-to-end vehicle and license plate detection with multi-branch attention neural network. IEEE Trans. Intell. Transp. Syst. 21(9), 3686\u20133695 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1997_CR2","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.patrec.2018.09.026","volume":"130","author":"D Wang","year":"2020","unstructured":"Wang, D., et al.: LPR-Net: recognizing Chinese license plate in complex environments. Pattern Recogn. Lett. 130, 148\u2013156 (2020)","journal-title":"Pattern Recogn. Lett."},{"unstructured":"Chenyang, H.: Research on the Method of License Plate Recognition Based on Image Processing (2018)","key":"1997_CR3"},{"doi-asserted-by":"crossref","unstructured":"Izidio, D.M., et al.: An embedded automatic license plate recognition system using deep learning. Design Automation for Embedded Systems, 1\u201321 (2019)","key":"1997_CR4","DOI":"10.1007\/s10617-019-09230-5"},{"doi-asserted-by":"crossref","unstructured":"Sutaji, D., Husenti, N.: Digital image processing for character detection of captcha login internet banking image using matching template. J. Phys. Conf. Ser. (2019)","key":"1997_CR5","DOI":"10.1088\/1742-6596\/1179\/1\/012115"},{"issue":"19","key":"1997_CR6","first-page":"65","volume":"43","author":"Y Wenfeng","year":"2020","unstructured":"Wenfeng, Y., et al.: Research on vehicle license plate character segmentation and recognition technology. Mod. Electron. Technol. 43(19), 65\u201369 (2020)","journal-title":"Mod. Electron. Technol."},{"issue":"03","key":"1997_CR7","first-page":"42","volume":"34","author":"RS Meng Xianghuan","year":"2020","unstructured":"Meng Xianghuan, R.S., Yuzu, Z., Ya, C., Sitao, C.: License plate character recognition method based on TensorFlow. J. Shanghai Univ. Eng. Sci. 34(03), 42\u201347 (2020)","journal-title":"J. Shanghai Univ. Eng. Sci."},{"issue":"99","key":"1997_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2019.2941596","volume":"PP","author":"X Lei","year":"2019","unstructured":"Lei, X., Pan, H., Huang, X.: A dilated CNN model for image classification. IEEE Access PP(99), 1\u20131 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"1997_CR9","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1016\/j.patcog.2011.09.021","volume":"45","author":"XX Niu","year":"2012","unstructured":"Niu, X.X., Suen, C.Y.: A novel hybrid CNN\u2013SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318\u20131325 (2012)","journal-title":"Pattern Recogn."},{"unstructured":"Peng, Z.: Research on license plate recognition system based on SVM (2018)","key":"1997_CR10"},{"issue":"2","key":"1997_CR11","doi-asserted-by":"publisher","first-page":"124914","DOI":"10.1016\/j.jmaa.2020.124914","volume":"497","author":"J Zeng","year":"2021","unstructured":"Zeng, J., et al.: Generalization ability of online pairwise support vector machine. J. Math. Anal. Appl. 497(2), 124914 (2021)","journal-title":"J. Math. Anal. Appl."},{"key":"1997_CR12","doi-asserted-by":"publisher","first-page":"107683","DOI":"10.1016\/j.patcog.2020.107683","volume":"111","author":"S Peng","year":"2021","unstructured":"Peng, S., et al.: Robust semi-supervised nonnegative matrix factorization for image clustering. Pattern Recogn. 111, 107683 (2021)","journal-title":"Pattern Recogn."},{"doi-asserted-by":"crossref","unstructured":"Xu, Z. et al.: Towards end-to-end license plate detection and recognition: a large dataset and baseline. In: Proceedings of the European conference on computer vision (ECCV) (2018)","key":"1997_CR13","DOI":"10.1007\/978-3-030-01261-8_16"},{"doi-asserted-by":"crossref","unstructured":"Yamashita, Y., Wakahara, T.: k-NN classification of handwritten characters using a new distortion-tolerant matching measure. In: International conference on pattern recognition (2014)","key":"1997_CR14","DOI":"10.1109\/ICPR.2014.54"},{"issue":"1","key":"1997_CR15","first-page":"47","volume":"38","author":"Z Jiajia","year":"2015","unstructured":"Jiajia, Z., et al.: Application of SVD and SVM superposition algorithm in image recognition of glass thermometer. Electron. Meas. Technol. 38(1), 47\u201350 (2015)","journal-title":"Electron. Meas. Technol."},{"doi-asserted-by":"crossref","unstructured":"Lok, U.-W., et al.: Real time SVD-based clutter filtering using randomized singular value decomposition and spatial downsampling for micro-vessel imaging on a Verasonics ultrasound system. Ultrasonics. 107, 106163(2020)","key":"1997_CR16","DOI":"10.1016\/j.ultras.2020.106163"},{"key":"1997_CR17","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.patrec.2019.02.018","volume":"122","author":"SM Atif","year":"2019","unstructured":"Atif, S.M., Qazi, S., Gillis, N.: Improved SVD-based initialization for nonnegative matrix factorization using low-rank correction. Pattern Recogn. Lett. 122, 53\u201359 (2019)","journal-title":"Pattern Recogn. Lett."},{"issue":"12","key":"1997_CR18","doi-asserted-by":"publisher","first-page":"5017","DOI":"10.1109\/TIP.2015.2475625","volume":"24","author":"TH Chan","year":"2015","unstructured":"Chan, T.H., et al.: PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017\u20135032 (2015)","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Chunikhina, E., Raich, R., Nguyen, T.: Performance analysis for matrix completion via iterative hard-thresholded SVD. In Statistical Signal Processing (2014)","key":"1997_CR19","DOI":"10.1109\/SSP.2014.6884658"},{"issue":"8","key":"1997_CR20","doi-asserted-by":"publisher","first-page":"5040","DOI":"10.1109\/TIT.2014.2323359","volume":"60","author":"M Gavish","year":"2014","unstructured":"Gavish, M., Donoho, D.L.: The optimal hard threshold for singular values is 4\/sqrt(3). IEEE Trans Inf Theory 60(8), 5040\u20135053 (2014)","journal-title":"IEEE Trans Inf Theory"},{"unstructured":"Donoho, D.L.: G.M. Code supplement to \"the optimal hard threshold for singular values is 4\/sqrt(3)\". https:\/\/purl.stanford.edu\/vg705qn9070 (2014)","key":"1997_CR21"},{"issue":"7","key":"1997_CR22","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.1134\/S1070363219070144","volume":"89","author":"N Bondarev","year":"2019","unstructured":"Bondarev, N.: Artificial neural network and multiple linear regression for prediction and classification of sustainability of sodium and potassium coronates. Russ. J. Gen. Chem. 89(7), 1438\u20131446 (2019)","journal-title":"Russ. J. Gen. Chem."},{"key":"1997_CR23","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.patrec.2018.12.012","volume":"130","author":"Y Mu","year":"2020","unstructured":"Mu, Y., et al.: Weighted tensor nuclear norm minimization for tensor completion using tensor-SVD. Pattern Recogn. Lett. 130, 4\u201311 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"1997_CR24","doi-asserted-by":"publisher","first-page":"107319","DOI":"10.1016\/j.sigpro.2019.107319","volume":"167","author":"A Wang","year":"2020","unstructured":"Wang, A., Jin, Z., Tang, G.: Robust tensor decomposition via t-SVD: near-optimal statistical guarantee and scalable algorithms. Signal Process. 167, 107319 (2020)","journal-title":"Signal Process."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-021-01997-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-021-01997-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-021-01997-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T10:47:04Z","timestamp":1645094824000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-021-01997-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,10]]},"references-count":24,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["1997"],"URL":"https:\/\/doi.org\/10.1007\/s11760-021-01997-0","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2021,8,10]]},"assertion":[{"value":"3 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}