{"id":"https://openalex.org/W4415594089","doi":"https://doi.org/10.1109/tmm.2025.3623548","title":"Balancing Optimization Strategies and Practical Goals: An Efficient Scene Text Detector","display_name":"Balancing Optimization Strategies and Practical Goals: An Efficient Scene Text Detector","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W4415594089","doi":"https://doi.org/10.1109/tmm.2025.3623548"},"language":null,"primary_location":{"id":"doi:10.1109/tmm.2025.3623548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3623548","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","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/A5008020379","display_name":"Xu Han","orcid":"https://orcid.org/0009-0005-6489-719X"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Han","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi’an, China","School of Computer Science, and School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0009-0005-6489-719X","affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Computer Science, and School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077254624","display_name":"Chuang Yang","orcid":"https://orcid.org/0000-0002-4086-8387"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuang Yang","raw_affiliation_strings":["School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China","School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-4086-8387","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Junyu Gao","orcid":"https://orcid.org/0000-0001-6000-8168"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junyu Gao","raw_affiliation_strings":["School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China","School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-6000-8168","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":null,"display_name":"Qi Wang","orcid":"https://orcid.org/0000-0002-7028-4956"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Wang","raw_affiliation_strings":["School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China","School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-7028-4956","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27278406,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"426","last_page":"438"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.8378999829292297,"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":0.8378999829292297,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.8032000064849854,"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"}},{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.7439000010490417,"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/filter","display_name":"Filter (signal processing)","score":0.6456999778747559},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.5853999853134155},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5724999904632568},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5620999932289124},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5453000068664551},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5031999945640564},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4634999930858612},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.43810001015663147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8723000288009644},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.6456999778747559},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6018999814987183},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.5853999853134155},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5724999904632568},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5620999932289124},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5453000068664551},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5031999945640564},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.43810001015663147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4262999892234802},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3449000120162964},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3345000147819519},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3296999931335449},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C2983812711","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Text recognition","level":3,"score":0.28839999437332153},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.28110000491142273},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27730000019073486},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.2644999921321869}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2025.3623548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3623548","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1964179093","display_name":null,"funder_award_id":"U21B2041","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5992145127","display_name":null,"funder_award_id":"62471394","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1972065312","https://openalex.org/W1988461287","https://openalex.org/W2144554289","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2550687635","https://openalex.org/W2565639579","https://openalex.org/W2593539516","https://openalex.org/W2601564443","https://openalex.org/W2605982830","https://openalex.org/W2784050770","https://openalex.org/W2785383245","https://openalex.org/W2810028092","https://openalex.org/W2902494497","https://openalex.org/W2953606406","https://openalex.org/W2962810613","https://openalex.org/W2963150697","https://openalex.org/W2963299604","https://openalex.org/W2963647456","https://openalex.org/W2966926453","https://openalex.org/W2967615747","https://openalex.org/W2991626090","https://openalex.org/W2998621280","https://openalex.org/W3012138014","https://openalex.org/W3034514377","https://openalex.org/W3034792612","https://openalex.org/W3035679705","https://openalex.org/W3093046205","https://openalex.org/W3093861118","https://openalex.org/W3138649552","https://openalex.org/W3154615938","https://openalex.org/W3159307593","https://openalex.org/W3172799005","https://openalex.org/W3176754135","https://openalex.org/W3181016597","https://openalex.org/W3186906052","https://openalex.org/W3189348500","https://openalex.org/W3203431461","https://openalex.org/W3211070770","https://openalex.org/W4212820886","https://openalex.org/W4214922754","https://openalex.org/W4221097663","https://openalex.org/W4283692648","https://openalex.org/W4285263829","https://openalex.org/W4289792721","https://openalex.org/W4293203016","https://openalex.org/W4293679656","https://openalex.org/W4296872795","https://openalex.org/W4312714895","https://openalex.org/W4321021053","https://openalex.org/W4379984742","https://openalex.org/W4380839071","https://openalex.org/W4382464147","https://openalex.org/W4383113591","https://openalex.org/W4385292311","https://openalex.org/W4386180224","https://openalex.org/W4389076425","https://openalex.org/W4392470567","https://openalex.org/W4393149654","https://openalex.org/W4403722763","https://openalex.org/W4404056889","https://openalex.org/W4405778871","https://openalex.org/W4405778935"],"related_works":[],"abstract_inverted_index":{"Scene":[0],"text":[1,18,48,174,205],"reading":[2],"is":[3,90,92,99],"a":[4,13,51,69,120,127],"crucial":[5],"task":[6,15],"for":[7,32],"scene":[8,17],"understanding.":[9],"Text":[10],"detection,":[11],"as":[12,50],"fundamental":[14],"in":[16],"reading,":[19],"has":[20],"recently":[21],"garnered":[22],"significant":[23],"attention.":[24],"Among":[25],"various":[26,176],"approaches,":[27],"segmentation-based":[28],"methods":[29,45,80,180],"stand":[30],"out":[31],"their":[33],"flexible":[34],"pixel-level":[35,85,185,193],"prediction":[36],"capabilities.":[37],"However,":[38],"two":[39],"main":[40],"issues":[41],"remain.":[42],"1)":[43],"These":[44],"treat":[46,151],"all":[47],"instances":[49,61,152],"pixel":[52],"set":[53],"during":[54],"training,":[55],"causing":[56],"the":[57,64,71,75,105,148,158,170,187,201,213,216],"features":[58,143],"of":[59,95,153,204,209,215],"large-scale":[60],"to":[62,132,150,172,195],"dominate":[63],"model":[65,149,171],"optimization":[66,72,134],"process.":[67],"As":[68],"result,":[70],"deviates":[73,103],"from":[74,104,175,224],"instance-level":[76,142,191],"objectives.":[77],"2)":[78],"Segmentation":[79],"filter":[81,182],"candidates":[82,183],"based":[83],"on":[84],"class":[86],"scores,":[87],"whereas":[88],"what":[89],"needed":[91],"an":[93,97,114],"evaluation":[94],"whether":[96],"instance":[98],"text,":[100],"which":[101],"also":[102],"original":[106,202],"goals.":[107,138],"To":[108],"address":[109],"these":[110],"issues,":[111],"we":[112],"propose":[113],"Instance-Equal":[115],"Feature":[116,122],"Guide":[117],"Module":[118,124],"(IEFGM),":[119],"Cross-Level":[121],"Interaction":[123],"(CLIFM),":[125],"and":[126,144,192],"Pixel-Instance":[128],"Fusion":[129],"Discriminator":[130],"(PIFD)":[131],"balance":[133],"strategies":[135],"with":[136,200],"practical":[137],"The":[139,161],"IEFGM":[140],"introduces":[141],"positional":[145],"information,":[146],"guiding":[147],"different":[154,167,225],"scales":[155],"equally":[156],"at":[157],"feature":[159,164],"level.":[160],"CLIFM":[162],"encourages":[163],"interaction":[165],"across":[166,221],"levels,":[168],"enabling":[169],"recognize":[173],"perspectives.":[177],"Unlike":[178],"existing":[179,232],"that":[181,228],"using":[184],"results,":[186],"PIFD":[188],"integrates":[189],"both":[190],"information":[194],"identify":[196],"candidate":[197],"regions,":[198],"aligning":[199],"goals":[203],"detection.":[206],"A":[207],"series":[208],"ablation":[210],"studies":[211],"demonstrates":[212],"effectiveness":[214],"proposed":[217],"modules.":[218],"Extensive":[219],"experiments":[220],"six":[222],"datasets":[223],"scenes":[226],"demonstrate":[227],"our":[229],"method":[230],"outperforms":[231],"state-of-the-art":[233],"approaches.":[234]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-28T00:00:00"}