{"id":"https://openalex.org/W4399199183","doi":"https://doi.org/10.1016/j.procs.2024.04.267","title":"An Efficient Deep Learning Approach for Automatic License Plate Detection with Novel Feature Extraction","display_name":"An Efficient Deep Learning Approach for Automatic License Plate Detection with Novel Feature Extraction","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4399199183","doi":"https://doi.org/10.1016/j.procs.2024.04.267"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2024.04.267","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2024.04.267","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2024.04.267","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025696218","display_name":"G. Kothai","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133257","display_name":"KPR Institute of Engineering and Technology","ror":"https://ror.org/02q9f3a53","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210133257"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Kothai G","raw_affiliation_strings":["Department of CSE (AIML), KPR Institute of Engineering and Technology, Coimbatore and 641407, India"],"affiliations":[{"raw_affiliation_string":"Department of CSE (AIML), KPR Institute of Engineering and Technology, Coimbatore and 641407, India","institution_ids":["https://openalex.org/I4210133257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098954850","display_name":"E Povammal","orcid":null},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Povammal E","raw_affiliation_strings":["Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur and 60203, India"],"affiliations":[{"raw_affiliation_string":"Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur and 60203, India","institution_ids":["https://openalex.org/I145286018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078222586","display_name":"S. Amutha","orcid":null},"institutions":[{"id":"https://openalex.org/I98499257","display_name":"Kalasalingam Academy of Research and Education","ror":"https://ror.org/04fm2fn75","country_code":"IN","type":"education","lineage":["https://openalex.org/I98499257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amutha S","raw_affiliation_strings":["Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil and 626126, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil and 626126, India","institution_ids":["https://openalex.org/I98499257"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087184386","display_name":"V. Deepa","orcid":null},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepa V","raw_affiliation_strings":["Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur and 60203, India"],"affiliations":[{"raw_affiliation_string":"Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur and 60203, India","institution_ids":["https://openalex.org/I145286018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5025696218"],"corresponding_institution_ids":["https://openalex.org/I4210133257"],"apc_list":null,"apc_paid":null,"fwci":3.7313,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.93687533,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"235","issue":null,"first_page":"2822","last_page":"2832"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12546","display_name":"Smart Parking Systems Research","score":0.9739000201225281,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9523000121116638,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.9323318004608154},{"id":"https://openalex.org/keywords/license","display_name":"License","score":0.8314811587333679},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6420478820800781},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5276896953582764},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5233180522918701},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4916265606880188},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4737463891506195},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45757365226745605},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39967596530914307},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32358860969543457},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.05676490068435669}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9323318004608154},{"id":"https://openalex.org/C2780560020","wikidata":"https://www.wikidata.org/wiki/Q79719","display_name":"License","level":2,"score":0.8314811587333679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6420478820800781},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5276896953582764},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5233180522918701},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4916265606880188},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4737463891506195},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45757365226745605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39967596530914307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32358860969543457},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.05676490068435669},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2024.04.267","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2024.04.267","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2024.04.267","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2024.04.267","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2891062723","https://openalex.org/W2899554750","https://openalex.org/W3010947874","https://openalex.org/W3012482474","https://openalex.org/W3083930151","https://openalex.org/W3096345373","https://openalex.org/W3112791713","https://openalex.org/W3165740330","https://openalex.org/W3212188983","https://openalex.org/W4210903309","https://openalex.org/W4214603750","https://openalex.org/W4214757704","https://openalex.org/W4238377065","https://openalex.org/W4281648890","https://openalex.org/W6756386354"],"related_works":["https://openalex.org/W2606446052","https://openalex.org/W2036021480","https://openalex.org/W3195777957","https://openalex.org/W2382668227","https://openalex.org/W2348482143","https://openalex.org/W2024584030","https://openalex.org/W3104168426","https://openalex.org/W1603675680","https://openalex.org/W2343406053","https://openalex.org/W1983668675"],"abstract_inverted_index":{"In":[0],"the":[1,37,53,58,81,105,150,191,197],"domain":[2],"of":[3,57,83,108,120,137,182,193,199],"traffic":[4,173,200],"management,":[5],"road":[6],"toll":[7],"collection,":[8],"and":[9,17,31,55,76,175,185,202,207],"parking":[10],"lot":[11],"systems,":[12],"vehicle":[13],"number":[14],"plate":[15,29,111,128],"detection":[16,30,61,129],"identification":[18],"play":[19],"a":[20,44],"pivotal":[21],"role.":[22],"Unlike":[23],"conventional":[24],"methods":[25,141],"that":[26],"treat":[27],"license":[28,72,110,127],"character":[32,116],"recognition":[33],"as":[34,64,215],"separate":[35],"tasks,":[36],"system":[38,122],"simultaneously":[39],"addresses":[40],"both":[41],"challenges":[42],"within":[43],"single":[45],"neural":[46,183],"network.":[47],"Our":[48],"Proposed":[49],"methodology":[50],"capitalizes":[51],"on":[52,152],"efficiency":[54],"accuracy":[56,135],"one-stage":[59],"object":[60,210],"algorithm":[62],"known":[63],"YOLO":[65],"(You":[66],"Only":[67],"Look":[68],"Once)":[69],"to":[70,214,219],"locate":[71],"plates":[73,222],"under":[74],"diverse":[75],"challenging":[77],"conditions.":[78],"To":[79],"augment":[80],"quality":[82,107],"input":[84],"images":[85],"with":[86,204],"low":[87],"resolution":[88],"or":[89],"poor":[90],"clarity,":[91],"we":[92],"employ":[93],"super-resolution":[94],"generative":[95],"adversarial":[96],"networks":[97,184],"(SRGANs).":[98],"The":[99,126,179],"image":[100],"enhancement":[101],"process":[102],"substantially":[103],"improves":[104],"visual":[106],"captured":[109],"images,":[112],"facilitating":[113],"more":[114],"precise":[115],"recognition.":[117],"Quantitative":[118],"assessment":[119],"propounded":[121],"reveals":[123],"compelling":[124],"results.":[125],"component":[130],"achieves":[131],"an":[132],"outstanding":[133],"average":[134],"rate":[136],"98.5%,":[138],"surpassing":[139],"previous":[140],"by":[142],"15.2%.":[143],"This":[144],"comprehensive":[145],"approach":[146],"not":[147],"only":[148],"reduces":[149],"dependency":[151],"manual":[153],"labour":[154],"but":[155],"also":[156],"elevates":[157],"processing":[158],"precision.":[159,208],"It":[160],"seamlessly":[161],"integrates":[162],"into":[163],"existing":[164],"transportation":[165],"infrastructure,":[166],"resulting":[167],"in":[168,223],"heightened":[169],"operational":[170],"efficiency,":[171],"reduced":[172],"congestion,":[174],"enhanced":[176],"security":[177],"measures.":[178],"rapid":[180],"evolution":[181],"deep":[186],"learning":[187],"techniques":[188],"has":[189],"streamlined":[190],"deployment":[192],"such":[194],"applications,":[195],"revolutionizing":[196],"field":[198],"monitoring":[201],"management":[203],"unprecedented":[205],"ease":[206],"One-stage":[209],"detector,":[211],"widely":[212],"referred":[213],"YOLO,":[216],"is":[217],"used":[218],"find":[220],"licence":[221],"difficult":[224],"circumstances.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}
