{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:03:49Z","timestamp":1767981829372,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T00:00:00Z","timestamp":1683331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176037"],"award-info":[{"award-number":["62176037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021CDPFAT-09"],"award-info":[{"award-number":["2021CDPFAT-09"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XLYC1908007"],"award-info":[{"award-number":["XLYC1908007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019J11CY001"],"award-info":[{"award-number":["2019J11CY001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021JJ12GX028"],"award-info":[{"award-number":["2021JJ12GX028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Project of China Disabled Persons\u2019 Federation on Assistive Technology","award":["62176037"],"award-info":[{"award-number":["62176037"]}]},{"name":"Research Project of China Disabled Persons\u2019 Federation on Assistive Technology","award":["2021CDPFAT-09"],"award-info":[{"award-number":["2021CDPFAT-09"]}]},{"name":"Research Project of China Disabled Persons\u2019 Federation on Assistive Technology","award":["XLYC1908007"],"award-info":[{"award-number":["XLYC1908007"]}]},{"name":"Research Project of China Disabled Persons\u2019 Federation on Assistive Technology","award":["2019J11CY001"],"award-info":[{"award-number":["2019J11CY001"]}]},{"name":"Research Project of China Disabled Persons\u2019 Federation on Assistive Technology","award":["2021JJ12GX028"],"award-info":[{"award-number":["2021JJ12GX028"]}]},{"DOI":"10.13039\/501100018617","name":"Liaoning Revitalization Talents Program","doi-asserted-by":"publisher","award":["62176037"],"award-info":[{"award-number":["62176037"]}],"id":[{"id":"10.13039\/501100018617","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018617","name":"Liaoning Revitalization Talents Program","doi-asserted-by":"publisher","award":["2021CDPFAT-09"],"award-info":[{"award-number":["2021CDPFAT-09"]}],"id":[{"id":"10.13039\/501100018617","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018617","name":"Liaoning Revitalization Talents Program","doi-asserted-by":"publisher","award":["XLYC1908007"],"award-info":[{"award-number":["XLYC1908007"]}],"id":[{"id":"10.13039\/501100018617","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018617","name":"Liaoning Revitalization Talents Program","doi-asserted-by":"publisher","award":["2019J11CY001"],"award-info":[{"award-number":["2019J11CY001"]}],"id":[{"id":"10.13039\/501100018617","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018617","name":"Liaoning Revitalization Talents Program","doi-asserted-by":"publisher","award":["2021JJ12GX028"],"award-info":[{"award-number":["2021JJ12GX028"]}],"id":[{"id":"10.13039\/501100018617","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017683","name":"Dalian Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["62176037"],"award-info":[{"award-number":["62176037"]}],"id":[{"id":"10.13039\/501100017683","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017683","name":"Dalian Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["2021CDPFAT-09"],"award-info":[{"award-number":["2021CDPFAT-09"]}],"id":[{"id":"10.13039\/501100017683","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017683","name":"Dalian Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["XLYC1908007"],"award-info":[{"award-number":["XLYC1908007"]}],"id":[{"id":"10.13039\/501100017683","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017683","name":"Dalian Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["2019J11CY001"],"award-info":[{"award-number":["2019J11CY001"]}],"id":[{"id":"10.13039\/501100017683","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017683","name":"Dalian Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["2021JJ12GX028"],"award-info":[{"award-number":["2021JJ12GX028"]}],"id":[{"id":"10.13039\/501100017683","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The current technological world is growing rapidly and each aspect of life is being transformed toward automation for human comfort and reliability. With autonomous vehicle technology, the communication gap between the driver and the traditional vehicle is being reduced through multiple technologies and methods. In this regard, state-of-the-art methods have proposed several approaches for advanced driver assistance systems (ADAS) to meet the requirement of a level-5 autonomous vehicle. Consequently, this work explores the role of textual cues present in the outer environment for finding the desired locations and assisting the driver where to stop. Firstly, the driver inputs the keywords of the desired location to assist the proposed system. Secondly, the system will start sensing the textual cues present in the outer environment through natural language processing techniques. Thirdly, the system keeps matching the similar keywords input by the driver and the outer environment using similarity learning. Whenever the system finds a location having any similar keyword in the outer environment, the system informs the driver, slows down, and applies the brake to stop. The experimental results on four benchmark datasets show the efficiency and accuracy of the proposed system for finding the desired locations by sensing textual cues in autonomous vehicles.<\/jats:p>","DOI":"10.3390\/s23094537","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T02:29:22Z","timestamp":1683512962000},"page":"4537","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1174-8236","authenticated-orcid":false,"given":"Salahuddin","family":"Unar","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Yining","family":"Su","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Pengbo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Lin","family":"Teng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Yafei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Xianping","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, H., Waslander, S.L., Yang, T., Zhang, S., Xiong, G., and Liu, K. (2018). Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization. Sensors, 18.","DOI":"10.20944\/preprints201805.0164.v1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, Y., Ruan, R., Zhou, Z., Sun, A., and Luo, X. (2023). Positioning of Unmanned Underwater Vehicle Based on Autonomous Tracking Buoy. Sensors, 23.","DOI":"10.3390\/s23094398"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1007\/s10489-020-01801-5","article-title":"Transfer Learning Based Hybrid 2D-3D CNN for Traffic Sign Recognition and Semantic Road Detection Applied in Advanced Driver Assistance Systems","volume":"51","author":"Bayoudh","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"14378","DOI":"10.1109\/ACCESS.2019.2893481","article-title":"Ego-Lane Position Identification with Event Warning Applications","volume":"7","author":"Cheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, Z., Yuan, S., Yin, X., Li, X., and Tang, S. (2023). Research into Autonomous Vehicles Following and Obstacle Avoidance Based on Deep Reinforcement Learning Method under Map Constraints. Sensors, 23.","DOI":"10.3390\/s23020844"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gragnaniello, D., Greco, A., Saggese, A., Vento, M., and Vicinanza, A. (2023). Benchmarking 2D Multi-Object Detection and Tracking Algorithms in Autonomous Vehicle Driving Scenarios. Sensors, 23.","DOI":"10.3390\/s23084024"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Park, J., Cho, J., Lee, S., Bak, S., and Kim, Y. (2023). An Automotive LiDAR Performance Test Method in Dynamic Driving Conditions. Sensors, 23.","DOI":"10.3390\/s23083892"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Giulietti, F., Dahia, K., Statheros, T., Innocente, M., Li, S., Frey, M., and Gauterin, F. (2023). Model-Based Condition Monitoring of the Sensors and Actuators of an Electric and Automated Vehicle. Sensors, 23.","DOI":"10.3390\/s23020887"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MCE.2018.2828440","article-title":"Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles","volume":"7","author":"Kukkala","year":"2018","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TCST.2022.3174511","article-title":"Autonomous Vehicle Kinematics and Dynamics Synthesis for Sideslip Angle Estimation Based on Consensus Kalman Filter","volume":"31","author":"Xia","year":"2023","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tsai, J., Chang, C.-C., and Li, T. (2023). Autonomous Driving Control Based on the Technique of Semantic Segmentation. Sensors, 23.","DOI":"10.3390\/s23020895"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10668","DOI":"10.1109\/TVT.2020.2983738","article-title":"IMU-Based Automated Vehicle Body Sideslip Angle and Attitude Estimation Aided by GNSS Using Parallel Adaptive Kalman Filters","volume":"69","author":"Xiong","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107993","DOI":"10.1016\/j.ymssp.2021.107993","article-title":"Estimation on IMU Yaw Misalignment by Fusing Information of Automotive Onboard Sensors","volume":"162","author":"Xia","year":"2022","journal-title":"Mech. Syst. Signal. Process"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alghamdi, A.S., Saeed, A., Kamran, M., Mursi, K.T., and Almukadi, W.S. (2023). Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms. Electronics, 12.","DOI":"10.3390\/electronics12020280"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dauptain, X., Kon\u00e9, A., Grolleau, D., Cerezo, V., Gennesseaux, M., and Do, M.T. (2022). Conception of a High-Level Perception and Localization System for Autonomous Driving. Sensors, 22.","DOI":"10.3390\/s22249661"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhao, L., Wei, Z., Li, Y., Jin, J., and Li, X. (2023). SEDG-Yolov5: A Lightweight Traffic Sign Detection Model Based on Knowledge Distillation. Electronics, 12.","DOI":"10.3390\/electronics12020305"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wei, Z., Zhang, F., Chang, S., Liu, Y., Wu, H., and Feng, Z. (2022). MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review. Sensors, 22.","DOI":"10.3390\/s22072542"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Miao, L., Chen, S.F., Hsu, Y.L., and Hua, K.L. (2022). How Does C-V2X Help Autonomous Driving to Avoid Accidents?. Sensors, 22.","DOI":"10.3390\/s22020686"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6778","DOI":"10.1109\/JSEN.2017.2746184","article-title":"A Novel Trail Detection and Scene Understanding Framework for a Quadrotor UAV with Monocular Vision","volume":"17","author":"Liu","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TSMC.2018.2868372","article-title":"Scene Understanding in Deep Learning-Based End-to-End Controllers for Autonomous Vehicles","volume":"49","author":"Yang","year":"2019","journal-title":"IEEE Trans. Syst. Man. Cybern. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TITS.2017.2750087","article-title":"3-D Surround View for Advanced Driver Assistance Systems","volume":"19","author":"Gao","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8085","DOI":"10.1109\/JSTARS.2022.3206399","article-title":"YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning","volume":"15","author":"Liu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth. Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104120","DOI":"10.1016\/j.trc.2023.104120","article-title":"An Automated Driving Systems Data Acquisition and Analytics Platform","volume":"151","author":"Xia","year":"2023","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114675","DOI":"10.1016\/j.eswa.2021.114675","article-title":"A Dynamic Cooperative Lane-Changing Model for Connected and Autonomous Vehicles with Possible Accelerations of a Preceding Vehicle","volume":"173","author":"Wang","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1109\/TITS.2020.2974495","article-title":"Realization and Evaluation of an Instructor-Like Assistance System for Collision Avoidance","volume":"22","author":"Chen","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"114581","DOI":"10.1016\/j.eswa.2021.114581","article-title":"Multi-Attribute Decision Making on Mitigating a Collision of an Autonomous Vehicle on Motorways","volume":"171","author":"Gilbert","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6818","DOI":"10.1109\/JSEN.2022.3150073","article-title":"Improved Vehicle Localization Using On-Board Sensors and Vehicle Lateral Velocity","volume":"22","author":"Gao","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"21675","DOI":"10.1109\/JSEN.2021.3059050","article-title":"Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic","volume":"21","author":"Liu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, B., Shi, H., Chen, L., Wang, X., Wang, G., Zhong, F.A., Wang, B., Shi, H., Chen, L., and Wang, X. (2023). A Recognition Method for Road Hypnosis Based on Physiological Characteristics. Sensors, 23.","DOI":"10.3390\/s23073404"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.3390\/smartcities6020056","article-title":"Camera-Based Smart Parking System Using Perspective Transformation","volume":"6","author":"Liu","year":"2023","journal-title":"Smart Cities"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhang, L., Lou, R., and Li, X. (2023). Recognition of Lane Changing Maneuvers for Vehicle Driving Safety. Electronics, 12.","DOI":"10.3390\/electronics12061456"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yan, Z., Yang, B., Wang, Z., Nakano, K.A., Valero, F., Yan, Z., Yang, B., Wang, Z., and Nakano, K. (2023). A Predictive Model of a Driver\u2019s Target Trajectory Based on Estimated Driving Behaviors. Sensors, 23.","DOI":"10.3390\/s23031405"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.imavis.2004.02.006","article-title":"Robust Wide-Baseline Stereo from Maximally Stable Extremal Regions","volume":"22","author":"Matas","year":"2004","journal-title":"Image Vis. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1007\/s00521-016-2223-x","article-title":"Evolving Weighting Schemes for the Bag of Visual Words","volume":"28","author":"Escalante","year":"2017","journal-title":"Neural. Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Farin, G.E., and Hansford, D. (2000). The Essentials of CAGD, A.K. Peters.","DOI":"10.1201\/9781439864111"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/TPAMI.2015.2496234","article-title":"Real-Time Lexicon-Free Scene Text Localization and Recognition","volume":"38","author":"Neumann","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1049\/iet-ipr.2018.5277","article-title":"Detected Text-Based Image Retrieval Approach for Textual Images","volume":"13","author":"Unar","year":"2019","journal-title":"IET Image Process"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1109\/TIP.2010.2070803","article-title":"A Hybrid Approach to Detect and Localize Texts in Natural Scene Images","volume":"20","author":"Pan","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1016\/j.patcog.2014.10.022","article-title":"A Flexible Framework for Online Document Segmentation by Pairwise Stroke Distance Learning","volume":"48","author":"Delaye","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_40","unstructured":"Niesler, T.R., and Woodland, P.C. (1996, January 9). A Variable-Length Category-Based n-Gram Language Model. Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, Atlanta, GA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1093\/comjnl\/bxy047","article-title":"Perceptual Image Hashing with Weighted DWT Features for Reduced-Reference Image Quality Assessment","volume":"61","author":"Tang","year":"2018","journal-title":"Comput. J."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, H., Bai, X., Yang, M., Zhu, S., Wang, J., and Liu, W. (2021, January 20\u201325). Scene Text Retrieval via Joint Text Detection and Similarity Learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00453"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Park, C., and Park, S. (2023). Performance Evaluation of Zone-Based In-Vehicle Network Architecture for Autonomous Vehicles. Sensors, 23.","DOI":"10.3390\/s23020669"},{"key":"ref_44","unstructured":"Kai, W., Babenko, B., and Belongie, S. (2011, January 6\u201313). End-to-End Scene Text Recognition. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., Bigorda, L.G.I., Mestre, S.R., Mas, J., Mota, D.F., Almazan, J.A., and De Las Heras, L.P. (2013, January 25\u201328). ICDAR 2013 Robust Reading Competition. Proceedings of the 12th International Conference on Document Analysis and Recognition, Washington, DC, USA.","DOI":"10.1109\/ICDAR.2013.221"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ch\u2019Ng, C.K., and Chan, C.S. (2017, January 9\u201315). Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition. Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan.","DOI":"10.1109\/ICDAR.2017.157"},{"key":"ref_47","unstructured":"Yao, C., Bai, X., Liu, W., Ma, Y., and Tu, Z. (2012, January 16\u201321). Detecting Texts of Arbitrary Orientations in Natural Images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_48","unstructured":"Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., and Young, R. (2003, January 3\u20136). ICDAR 2003 Robust Reading Competitions. Proceedings of the 7th International Conference on Document Analysis and Recognition, Edinburgh, UK."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.inffus.2018.03.006","article-title":"Visual and Textual Information Fusion Using Kernel Method for Content Based Image Retrieval","volume":"44","author":"Unar","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.image.2016.10.003","article-title":"Text Detection in Scene Images Based on Exhaustive Segmentation","volume":"50","author":"Wei","year":"2017","journal-title":"Signal Process Image Commun."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.knosys.2019.05.001","article-title":"A Decisive Content Based Image Retrieval Approach for Feature Fusion in Visual and Textual Images","volume":"179","author":"Unar","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.neucom.2015.10.105","article-title":"Scene Text Localization Using Edge Analysis and Feature Pool","volume":"175","author":"Yu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"107767","DOI":"10.1016\/j.knosys.2021.107767","article-title":"PRPN: Progressive Region Prediction Network for Natural Scene Text Detection","volume":"236","author":"Zhong","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lyu, P., Yao, C., Wu, W., Yan, S., and Bai, X. (2018, January 18\u201322). Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00788"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7904","DOI":"10.1109\/TIP.2020.3008863","article-title":"HAM: Hidden Anchor Mechanism for Scene Text Detection","volume":"29","author":"Hou","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, X., Jiang, Y., Luo, Z., Liu, C.L., Choi, H., and Kim, S. (2019, January 15\u201320). Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00661"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xie, H., Zha, Z., Xing, M., Fu, Z., and Zhang, Y. (2020, January 16\u201318). Contournet: Taking a Further Step toward Accurate Arbitrary-Shaped Scene Text Detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01177"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Hou, W., Lu, T., Yu, G., and Shao, S. (2019, January 15\u201320). Shape Robust Text Detection with Progressive Scale Expansion Network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00956"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"11474","DOI":"10.1609\/aaai.v34i07.6812","article-title":"Real-Time Scene Text Detection with Differentiable Binarization","volume":"Volume 34","author":"Liao","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, C., Liang, B., Huang, Z., En, M., Han, J., Ding, E., and Ding, X. (2019, January 15\u201320). Look More than Once: An Accurate Detector for Text of Arbitrary Shapes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01080"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Shi, B., Bai, X., and Belongie, S. (2017, January 21\u201326). Detecting Oriented Text in Natural Images by Linking Segments. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.371"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Mishra, A., Alahari, K., and Jawahar, C.V. (2013, January 1\u20138). Image Retrieval Using Textual Cues. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.378"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Neumann, L., and Matas, J. (2012, January 16\u201321). Real-Time Scene Text Localization and Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248097"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4537\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:30:42Z","timestamp":1760124642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4537"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,6]]},"references-count":63,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094537"],"URL":"https:\/\/doi.org\/10.3390\/s23094537","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,6]]}}}