{"id":"https://openalex.org/W4403486806","doi":"https://doi.org/10.3233/faia241053","title":"License Plate Images Generation with Diffusion Models","display_name":"License Plate Images Generation with Diffusion Models","publication_year":2024,"publication_date":"2024-10-16","ids":{"openalex":"https://openalex.org/W4403486806","doi":"https://doi.org/10.3233/faia241053"},"language":"en","primary_location":{"id":"doi:10.3233/faia241053","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia241053","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241053","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241053","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114301680","display_name":"Mariia Shpir","orcid":null},"institutions":[{"id":"https://openalex.org/I112412981","display_name":"National University of Kyiv-Mohyla Academy","ror":"https://ror.org/03wfca816","country_code":"UA","type":"education","lineage":["https://openalex.org/I112412981"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Mariia Shpir","raw_affiliation_strings":["National University of Kyiv-Mohyla Academy, Kyiv, Ukraine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Kyiv-Mohyla Academy, Kyiv, Ukraine","institution_ids":["https://openalex.org/I112412981"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050262088","display_name":"Nadiya Shvai","orcid":"https://orcid.org/0000-0001-8194-6196"},"institutions":[{"id":"https://openalex.org/I112412981","display_name":"National University of Kyiv-Mohyla Academy","ror":"https://ror.org/03wfca816","country_code":"UA","type":"education","lineage":["https://openalex.org/I112412981"]},{"id":"https://openalex.org/I4210123827","display_name":"Vinci (France)","ror":"https://ror.org/03e278q38","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210123827"]}],"countries":["FR","UA"],"is_corresponding":false,"raw_author_name":"Nadiya Shvai","raw_affiliation_strings":["Cyclope.ai, VINCI Autoroutes, Paris, France","National University of Kyiv-Mohyla Academy, Kyiv, Ukraine"],"raw_orcid":"https://orcid.org/0000-0001-8194-6196","affiliations":[{"raw_affiliation_string":"Cyclope.ai, VINCI Autoroutes, Paris, France","institution_ids":["https://openalex.org/I4210123827"]},{"raw_affiliation_string":"National University of Kyiv-Mohyla Academy, Kyiv, Ukraine","institution_ids":["https://openalex.org/I112412981"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067068847","display_name":"Amir Nakib","orcid":"https://orcid.org/0000-0001-9620-9324"},"institutions":[{"id":"https://openalex.org/I197681013","display_name":"Universit\u00e9 Paris-Est Cr\u00e9teil","ror":"https://ror.org/05ggc9x40","country_code":"FR","type":"education","lineage":["https://openalex.org/I197681013"]},{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]},{"id":"https://openalex.org/I4210123827","display_name":"Vinci (France)","ror":"https://ror.org/03e278q38","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210123827"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Amir Nakib","raw_affiliation_strings":["Cyclope.ai, VINCI Autoroutes, Paris, France","University Paris Est Cr\u00e9teil, Laboratoire LISSI, Vitry sur Seine, France"],"raw_orcid":"https://orcid.org/0000-0001-9620-9324","affiliations":[{"raw_affiliation_string":"Cyclope.ai, VINCI Autoroutes, Paris, France","institution_ids":["https://openalex.org/I4210123827"]},{"raw_affiliation_string":"University Paris Est Cr\u00e9teil, Laboratoire LISSI, Vitry sur Seine, France","institution_ids":["https://openalex.org/I2800365227","https://openalex.org/I197681013"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7468,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.94001387,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9484999775886536,"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":0.9484999775886536,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.800605297088623},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7387360334396362},{"id":"https://openalex.org/keywords/license","display_name":"License","score":0.6747564077377319},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5552173852920532},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48520591855049133},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.45149892568588257},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.44994229078292847},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4352802038192749},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.41245388984680176},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3635219931602478},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32564955949783325},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08625394105911255}],"concepts":[{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.800605297088623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7387360334396362},{"id":"https://openalex.org/C2780560020","wikidata":"https://www.wikidata.org/wiki/Q79719","display_name":"License","level":2,"score":0.6747564077377319},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5552173852920532},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48520591855049133},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.45149892568588257},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.44994229078292847},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4352802038192749},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.41245388984680176},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3635219931602478},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32564955949783325},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08625394105911255},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3233/faia241053","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia241053","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241053","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},{"id":"pmh:oai:arXiv.org:2501.03374","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2501.03374","pdf_url":"https://arxiv.org/pdf/2501.03374","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.3233/faia241053","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia241053","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA241053","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5600000023841858,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4403486806.pdf"},"referenced_works_count":0,"referenced_works":[],"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":{"Despite":[0,173],"the":[1,15,27,98,107,126,138,141,164,170,174,179,187,190],"evident":[2],"practical":[3],"importance":[4],"of":[5,17,97,106,118,125,128,140,152,166,189],"license":[6,54],"plate":[7],"recognition":[8],"(LPR),":[9],"corresponding":[10],"research":[11],"is":[12],"limited":[13],"by":[14,61,205],"volume":[16],"publicly":[18,156],"available":[19,157],"datasets":[20],"due":[21],"to":[22,51,199,208],"privacy":[23],"regulations":[24],"such":[25,110],"as":[26,42,111],"General":[28],"Data":[29],"Protection":[30],"Regulation":[31],"(GDPR).":[32],"To":[33],"address":[34],"this":[35,47],"challenge,":[36],"synthetic":[37,85,149,167,185,196],"data":[38,168,192,197],"generation":[39],"has":[40],"emerged":[41],"a":[43,72,79,103,148],"promising":[44],"approach.":[45],"In":[46,69],"paper,":[48],"we":[49,101,145],"propose":[50],"synthesize":[52],"realistic":[53],"plates":[55],"(LPs)":[56],"using":[57],"diffusion":[58,73,129],"models,":[59],"inspired":[60],"recent":[62],"advances":[63],"in":[64,202],"image":[65],"and":[66,83,95,116,184],"video":[67],"generation.":[68],"our":[70],"experiments":[71,161],"model":[74,108,180],"was":[75],"successfully":[76],"trained":[77,181],"on":[78],"Ukrainian":[80],"LP":[81,132,154],"dataset,":[82],"1000":[84],"images":[86],"were":[87],"generated":[88,99,142],"for":[89,131,169],"detailed":[90],"analysis.":[91],"Through":[92],"manual":[93],"classification":[94],"annotation":[96],"images,":[100,155],"performed":[102],"thorough":[104],"study":[105],"output,":[109],"success":[112],"rate,":[113],"character":[114],"distributions,":[115],"type":[117],"failures.":[119],"Our":[120],"contributions":[121],"include":[122],"experimental":[123],"validation":[124],"efficacy":[127],"models":[130],"synthesis,":[133],"along":[134],"with":[135,182,194],"insights":[136],"into":[137],"characteristics":[139],"data.":[143],"Furthermore,":[144],"have":[146],"prepared":[147],"dataset":[150],"consisting":[151],"10,000":[153],"at":[158],"https://zenodo.org/doi/10.5281/zenodo.13342102.":[159],"Conducted":[160],"empirically":[162],"confirm":[163],"usefulness":[165],"LPR":[171,203],"task.":[172],"initial":[175],"performance":[176],"gap":[177],"between":[178],"real":[183],"data,":[186],"expansion":[188],"training":[191],"set":[193],"pseudolabeled":[195],"leads":[198],"an":[200],"improvement":[201],"accuracy":[204],"3%":[206],"compared":[207],"baseline.":[209]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
