{"id":"https://openalex.org/W4396628261","doi":"https://doi.org/10.1145/3647649.3647697","title":"Orthogonal Feature Alignment Network for Cross-Domain Text Detection","display_name":"Orthogonal Feature Alignment Network for Cross-Domain Text Detection","publication_year":2024,"publication_date":"2024-01-19","ids":{"openalex":"https://openalex.org/W4396628261","doi":"https://doi.org/10.1145/3647649.3647697"},"language":"en","primary_location":{"id":"doi:10.1145/3647649.3647697","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647649.3647697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Image and Graphics Processing","raw_type":"proceedings-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/A5056463947","display_name":"Yong Hu","orcid":"https://orcid.org/0009-0007-4306-0205"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yong Hu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029828175","display_name":"Xueming Li","orcid":"https://orcid.org/0000-0003-1058-2799"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueming Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100333738","display_name":"Yue Zhang","orcid":"https://orcid.org/0000-0002-6327-5023"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5056463947"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05356498,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"301","last_page":"307"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9776999950408936,"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":0.9423999786376953,"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/computer-science","display_name":"Computer science","score":0.8628336191177368},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.7557638883590698},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7210037112236023},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6389067769050598},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6145352125167847},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6042041778564453},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5296363830566406},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48859721422195435},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.487490177154541},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4309000074863434},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37059858441352844}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8628336191177368},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.7557638883590698},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7210037112236023},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6389067769050598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6145352125167847},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6042041778564453},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5296363830566406},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48859721422195435},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.487490177154541},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4309000074863434},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37059858441352844},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3647649.3647697","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647649.3647697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Image and Graphics Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2343052201","https://openalex.org/W2467286621","https://openalex.org/W2605982830","https://openalex.org/W2873558679","https://openalex.org/W2962823940","https://openalex.org/W2963647456","https://openalex.org/W2963785012","https://openalex.org/W2987563462","https://openalex.org/W2990069979","https://openalex.org/W2991626090","https://openalex.org/W2991699366","https://openalex.org/W3034779842","https://openalex.org/W3035679705","https://openalex.org/W3082586907","https://openalex.org/W3181016597","https://openalex.org/W4212991279","https://openalex.org/W4312929132","https://openalex.org/W4312993742","https://openalex.org/W4319300551","https://openalex.org/W4319300809","https://openalex.org/W4319300947"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W1557094818","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W4246352526"],"abstract_inverted_index":{"Scene":[0],"text":[1,86,101,105,112],"detection":[2,129],"methods":[3],"based":[4],"on":[5,48,152],"deep":[6],"learning":[7],"have":[8],"achieved":[9],"remarkable":[10],"success.":[11],"To":[12],"address":[13],"the":[14,37,65,77,97,104,128,132,135,139,157,161],"laborious":[15],"and":[16,32,42,71,107,145,156],"time-consuming":[17],"process":[18],"of":[19,26,100,131,142,163],"manually":[20],"annotating":[21],"datasets,":[22,155],"a":[23,118],"large":[24],"amount":[25],"synthetic":[27,41,49,70],"data":[28,50],"has":[29],"been":[30],"created":[31],"utilized.":[33],"However,":[34],"due":[35],"to":[36,58,63,95,125],"domain":[38,66],"discrepancy":[39],"between":[40,69],"real":[43,59,72],"scene":[44,73],"data,":[45,74],"models":[46],"trained":[47],"may":[51],"suffer":[52],"from":[53],"performance":[54,130],"degradation":[55],"when":[56],"applied":[57],"scenes.":[60],"In":[61],"order":[62],"tackle":[64],"shift":[67],"issue":[68],"we":[75],"propose":[76],"Orthogonal":[78],"Feature":[79],"Alignment":[80],"Network":[81],"(OFAN)":[82],"specifically":[83],"designed":[84],"for":[85,111],"objects.":[87],"OFAN":[88,151],"incorporates":[89],"an":[90],"orthogonal":[91],"feature":[92],"enhancement":[93],"module":[94],"strengthen":[96],"edge":[98],"features":[99],"instances,":[102],"emphasizing":[103],"objects,":[106],"employs":[108],"adversarial":[109],"training":[110],"instance":[113],"alignment":[114],"across":[115],"domains.":[116],"Additionally,":[117],"multi-transform":[119],"self-training":[120],"mixture":[121],"technique":[122],"is":[123],"utilized":[124],"further":[126],"improve":[127],"model":[133],"in":[134],"target":[136],"domain,":[137],"mitigating":[138],"adverse":[140],"effects":[141],"false":[143,146],"positives":[144],"negatives.":[147],"We":[148],"extensively":[149],"evaluate":[150],"four":[153],"benchmark":[154],"experimental":[158],"results":[159],"demonstrate":[160],"effectiveness":[162],"our":[164],"approach.":[165]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
