{"id":"https://openalex.org/W4205735926","doi":"https://doi.org/10.1109/vcip53242.2021.9675323","title":"Multi-Dimension Aware Back Projection Network For Scene Text Detection","display_name":"Multi-Dimension Aware Back Projection Network For Scene Text Detection","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4205735926","doi":"https://doi.org/10.1109/vcip53242.2021.9675323"},"language":"en","primary_location":{"id":"doi:10.1109/vcip53242.2021.9675323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip53242.2021.9675323","pdf_url":null,"source":{"id":"https://openalex.org/S4363608378","display_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","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/A5035897243","display_name":"Yizhan Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yizhan Zhao","raw_affiliation_strings":["Tianjin University,School of Electrical and Information Engineering,Tianjin,China","School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University,School of Electrical and Information Engineering,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036035213","display_name":"Sumei Li","orcid":"https://orcid.org/0000-0002-4793-3161"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sumei Li","raw_affiliation_strings":["Tianjin University,School of Electrical and Information Engineering,Tianjin,China","School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University,School of Electrical and Information Engineering,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062490905","display_name":"Yongli Chang","orcid":"https://orcid.org/0000-0002-2803-6983"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongli Chang","raw_affiliation_strings":["Tianjin University,School of Electrical and Information Engineering,Tianjin,China","School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University,School of Electrical and Information Engineering,Tianjin,China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5035897243"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16474654,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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":0.9930999875068665,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9871000051498413,"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":0.786677896976471},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.75821453332901},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6992852687835693},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6369593143463135},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5879424214363098},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.584989070892334},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5623800158500671},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5062867403030396},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.49922966957092285},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47226276993751526},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.46489113569259644},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4512169361114502},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.41215765476226807},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.28120702505111694},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.1895657777786255},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15624916553497314},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11653146147727966}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786677896976471},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.75821453332901},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6992852687835693},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6369593143463135},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5879424214363098},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.584989070892334},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5623800158500671},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5062867403030396},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.49922966957092285},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47226276993751526},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.46489113569259644},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4512169361114502},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.41215765476226807},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28120702505111694},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.1895657777786255},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15624916553497314},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11653146147727966},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip53242.2021.9675323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip53242.2021.9675323","pdf_url":null,"source":{"id":"https://openalex.org/S4363608378","display_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G6802404304","display_name":null,"funder_award_id":"61971306,61520106002,6141262","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1817277359","https://openalex.org/W1972065312","https://openalex.org/W1988461287","https://openalex.org/W2108598243","https://openalex.org/W2144554289","https://openalex.org/W2194775991","https://openalex.org/W2343052201","https://openalex.org/W2550687635","https://openalex.org/W2565639579","https://openalex.org/W2593539516","https://openalex.org/W2605982830","https://openalex.org/W2752782242","https://openalex.org/W2785383245","https://openalex.org/W2810028092","https://openalex.org/W2884585870","https://openalex.org/W2963398399","https://openalex.org/W2963516811","https://openalex.org/W2963647456","https://openalex.org/W2963729050","https://openalex.org/W2964685115","https://openalex.org/W2967155990","https://openalex.org/W2998621280","https://openalex.org/W3020612720","https://openalex.org/W3027134841","https://openalex.org/W3034514377","https://openalex.org/W3035679705","https://openalex.org/W3101769104","https://openalex.org/W3106228955","https://openalex.org/W3106250896","https://openalex.org/W3181016597","https://openalex.org/W6642972425","https://openalex.org/W6676297131","https://openalex.org/W6681452975","https://openalex.org/W6729791593","https://openalex.org/W6748033758","https://openalex.org/W6753412334","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2376957272"],"abstract_inverted_index":{"Recently,":[0],"scene":[1],"text":[2,39],"detection":[3],"based":[4],"on":[5,107],"deep":[6],"learning":[7],"has":[8],"progressed":[9],"substantially.":[10],"Nevertheless,":[11],"most":[12],"previous":[13],"models":[14],"with":[15],"FPN":[16],"are":[17],"limited":[18],"by":[19,72],"the":[20,43,58,67,116],"drawback":[21,59],"of":[22,60,69,118],"sample":[23,61],"interpolation":[24,62],"algorithms,":[25],"which":[26,96],"fail":[27],"to":[28,41,56,87,100],"generate":[29,101],"high-quality":[30],"up-sampled":[31,70],"features.":[32],"Accordingly,":[33],"we":[34],"propose":[35],"an":[36],"end-to-end":[37],"trainable":[38],"detector":[40],"alleviate":[42,57],"above":[44],"dilemma.":[45],"Specifically,":[46],"a":[47,80],"Back":[48],"Projection":[49],"Enhanced":[50],"Up-sampling":[51],"(BPEU)":[52],"block":[53,84],"is":[54,85],"proposed":[55],"algorithms.":[63],"It":[64],"significantly":[65],"enhances":[66],"quality":[68],"features":[71,99],"employing":[73],"back":[74],"projection":[75],"and":[76,93,113],"detail":[77],"compensation.":[78],"Further-more,":[79],"Multi-Dimensional":[81],"Attention":[82],"(MDA)":[83],"devised":[86],"learn":[88],"different":[89],"knowledge":[90],"from":[91],"spatial":[92],"channel":[94],"dimensions,":[95],"intelligently":[97],"selects":[98],"more":[102],"discriminative":[103],"representations.":[104],"Experimental":[105],"results":[106],"three":[108],"benchmarks,":[109],"ICDAR2015,":[110],"ICDAR2017-":[111],"MLT":[112],"MSRA-TD500,":[114],"demonstrate":[115],"effectiveness":[117],"our":[119],"method.":[120]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
