{"id":"https://openalex.org/W4288391612","doi":"https://doi.org/10.1109/tcsvt.2022.3194835","title":"Robust Scene Text Detection for Partially Annotated Training Data","display_name":"Robust Scene Text Detection for Partially Annotated Training Data","publication_year":2022,"publication_date":"2022-07-28","ids":{"openalex":"https://openalex.org/W4288391612","doi":"https://doi.org/10.1109/tcsvt.2022.3194835"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2022.3194835","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3194835","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070077567","display_name":"Prateek Keserwani","orcid":"https://orcid.org/0000-0001-7611-6462"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prateek Keserwani","raw_affiliation_strings":["Indian Institute of Technology Roorkee, Roorkee, India"],"raw_orcid":"https://orcid.org/0000-0001-7611-6462","affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Roorkee, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036975174","display_name":"Rajkumar Saini","orcid":"https://orcid.org/0000-0001-8532-0895"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajkumar Saini","raw_affiliation_strings":["EISLAB Machine Learning, Lule&#x00E5; University of Technology, Lule&#x00E5;, Sweden"],"raw_orcid":"https://orcid.org/0000-0001-8532-0895","affiliations":[{"raw_affiliation_string":"EISLAB Machine Learning, Lule&#x00E5; University of Technology, Lule&#x00E5;, Sweden","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073619925","display_name":"Marcus Liwicki","orcid":"https://orcid.org/0000-0003-4029-6574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marcus Liwicki","raw_affiliation_strings":["EISLAB Machine Learning, Lule&#x00E5; University of Technology, Lule&#x00E5;, Sweden"],"raw_orcid":"https://orcid.org/0000-0003-4029-6574","affiliations":[{"raw_affiliation_string":"EISLAB Machine Learning, Lule&#x00E5; University of Technology, Lule&#x00E5;, Sweden","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036208317","display_name":"Partha Pratim Roy","orcid":"https://orcid.org/0000-0002-5735-5254"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Partha Pratim Roy","raw_affiliation_strings":["Indian Institute of Technology Roorkee, Roorkee, India"],"raw_orcid":"https://orcid.org/0000-0002-5735-5254","affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Roorkee, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.664,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.85102906,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"32","issue":"12","first_page":"8635","last_page":"8645"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":1.0,"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"}},{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9908000230789185,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9821000099182129,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.8361963033676147},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.8123273849487305},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6764681339263916},{"id":"https://openalex.org/keywords/text-detection","display_name":"Text detection","score":0.62909996509552},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5818315148353577},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5195925235748291},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4888603091239929},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3960554003715515},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3739519715309143},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33526670932769775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8361963033676147},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.8123273849487305},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6764681339263916},{"id":"https://openalex.org/C2983589003","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Text detection","level":3,"score":0.62909996509552},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5818315148353577},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5195925235748291},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4888603091239929},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3960554003715515},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3739519715309143},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33526670932769775},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2022.3194835","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3194835","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W117491841","https://openalex.org/W639708223","https://openalex.org/W654550266","https://openalex.org/W1521064364","https://openalex.org/W1522301498","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W2102348129","https://openalex.org/W2124404372","https://openalex.org/W2135231474","https://openalex.org/W2144554289","https://openalex.org/W2148214126","https://openalex.org/W2167460663","https://openalex.org/W2395709053","https://openalex.org/W2543927648","https://openalex.org/W2590822257","https://openalex.org/W2604735854","https://openalex.org/W2605982830","https://openalex.org/W2791490164","https://openalex.org/W2804077623","https://openalex.org/W2808867199","https://openalex.org/W2875814315","https://openalex.org/W2887500939","https://openalex.org/W2899771611","https://openalex.org/W2911575099","https://openalex.org/W2962984063","https://openalex.org/W2963521811","https://openalex.org/W2963697299","https://openalex.org/W2966434031","https://openalex.org/W2969510879","https://openalex.org/W2971609010","https://openalex.org/W2980358704","https://openalex.org/W2982391314","https://openalex.org/W2998621280","https://openalex.org/W3005400651","https://openalex.org/W3015679904","https://openalex.org/W3036586801","https://openalex.org/W3082397598","https://openalex.org/W3083321694","https://openalex.org/W3092156752","https://openalex.org/W3101611481","https://openalex.org/W3126207803","https://openalex.org/W3134523352","https://openalex.org/W3136424010","https://openalex.org/W3152635971","https://openalex.org/W3167225078","https://openalex.org/W3175854195","https://openalex.org/W3192658445","https://openalex.org/W4205474609","https://openalex.org/W4297801285","https://openalex.org/W6620707391","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6693514096","https://openalex.org/W6751661576","https://openalex.org/W6752402975","https://openalex.org/W6756040250","https://openalex.org/W6766318054","https://openalex.org/W6767249749","https://openalex.org/W6769659910","https://openalex.org/W6773926150","https://openalex.org/W6786728664"],"related_works":["https://openalex.org/W4317548404","https://openalex.org/W3022007134","https://openalex.org/W2130553454","https://openalex.org/W2087783760","https://openalex.org/W2033364610","https://openalex.org/W2797776314","https://openalex.org/W3163689946","https://openalex.org/W2153927146","https://openalex.org/W3104108945","https://openalex.org/W4390190783"],"abstract_inverted_index":{"This":[0],"article":[1],"analyzed":[2],"the":[3,38,41,74,88,91,105,108,118,136,158,173,191,212,218],"impact":[4],"of":[5,40,69,76,90,94,107,121],"training":[6,95,122,139,221],"data":[7,96,140],"containing":[8],"un-annotated":[9],"text":[10,17,22,30,55,99,111,128,143,170],"instances,":[11],"i.e.,":[12],"partial":[13,92,119],"annotation":[14,93,120],"in":[15,141,157,172],"scene":[16,54,98,110,142],"detection,":[18],"and":[19,73,82,197,214],"proposed":[20,126,146,205],"a":[21,33,66,80,127,150,183,199,208],"region":[23,129],"refinement":[24,130],"approach":[25],"to":[26,117,168],"address":[27],"it.":[28],"Scene":[29],"detection":[31,56,112],"is":[32,79],"problem":[34],"that":[35,104,132,165],"has":[36],"attracted":[37],"attention":[39],"research":[42],"community":[43],"for":[44,51,97,217],"decades.":[45],"Impressive":[46],"results":[47],"have":[48,102,125,179],"been":[49,180],"obtained":[50,156],"fully":[52],"supervised":[53],"with":[57,193],"recent":[58],"deep":[59],"learning":[60],"approaches.":[61],"These":[62],"approaches,":[63],"however,":[64],"need":[65],"vast":[67],"amount":[68],"completely":[70],"labeled":[71],"datasets,":[72],"creation":[75],"such":[77],"datasets":[78],"challenging":[81],"time-consuming":[83],"task.":[84],"Research":[85],"literature":[86],"lacks":[87],"analysis":[89],"detection.":[100,144],"We":[101,124],"found":[103],"performance":[106],"generic":[109],"method":[113,131,147,206],"drops":[114],"significantly":[115],"due":[116],"data.":[123,222],"provides":[133],"robustness":[134],"against":[135],"partially":[137,219],"annotated":[138,220],"The":[145,204],"works":[148],"as":[149],"two-tier":[151],"scheme.":[152],"Text-probable":[153],"regions":[154,171],"are":[155],"first":[159],"tier":[160],"by":[161,189],"applying":[162],"hybrid":[163],"loss":[164],"generates":[166],"pseudo-labels":[167],"refine":[169],"second-tier":[174],"during":[175],"training.":[176],"Extensive":[177],"experiments":[178],"conducted":[181],"on":[182,198],"dataset":[184],"generated":[185],"from":[186],"ICDAR":[187],"2015":[188],"dropping":[190],"annotations":[192],"various":[194],"drop":[195],"rates":[196],"publicly":[200],"available":[201],"SVT":[202],"dataset.":[203],"exhibits":[207],"significant":[209],"improvement":[210],"over":[211],"baseline":[213],"existing":[215],"approaches":[216]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
