{"id":"https://openalex.org/W4403332838","doi":"https://doi.org/10.1145/3686490.3686517","title":"Arbitrary-shape Scene Text Detection via Spatial Relationship Module and Subspace Attention Module","display_name":"Arbitrary-shape Scene Text Detection via Spatial Relationship Module and Subspace Attention Module","publication_year":2024,"publication_date":"2024-07-12","ids":{"openalex":"https://openalex.org/W4403332838","doi":"https://doi.org/10.1145/3686490.3686517"},"language":"en","primary_location":{"id":"doi:10.1145/3686490.3686517","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3686490.3686517","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 Signal Processing and Machine Learning","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/A5100716084","display_name":"Manli Wang","orcid":"https://orcid.org/0000-0001-5039-5723"},"institutions":[{"id":"https://openalex.org/I4210166499","display_name":"Henan Polytechnic University","ror":"https://ror.org/05vr1c885","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210166499"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Manli Wang","raw_affiliation_strings":["Institute of Physics and Electronic Information, Henan Polytechnic University, China"],"affiliations":[{"raw_affiliation_string":"Institute of Physics and Electronic Information, Henan Polytechnic University, China","institution_ids":["https://openalex.org/I4210166499"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114244878","display_name":"Zeya Dou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210166499","display_name":"Henan Polytechnic University","ror":"https://ror.org/05vr1c885","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210166499"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeya Dou","raw_affiliation_strings":["Institute of Physics and Electronic Information, Henan Polytechnic University, China"],"affiliations":[{"raw_affiliation_string":"Institute of Physics and Electronic Information, Henan Polytechnic University, China","institution_ids":["https://openalex.org/I4210166499"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103762642","display_name":"Chen Chen","orcid":"https://orcid.org/0009-0009-9971-4815"},"institutions":[{"id":"https://openalex.org/I4210166499","display_name":"Henan Polytechnic University","ror":"https://ror.org/05vr1c885","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210166499"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["Institute of Physics and Electronic Information, Henan Polytechnic University, China"],"affiliations":[{"raw_affiliation_string":"Institute of Physics and Electronic Information, Henan Polytechnic University, China","institution_ids":["https://openalex.org/I4210166499"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100716084"],"corresponding_institution_ids":["https://openalex.org/I4210166499"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1729659,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"183","last_page":"189"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9857000112533569,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9815999865531921,"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/subspace-topology","display_name":"Subspace topology","score":0.8378514051437378},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7169893980026245},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5475285649299622},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.511296272277832},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42528796195983887}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.8378514051437378},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7169893980026245},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5475285649299622},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.511296272277832},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42528796195983887}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3686490.3686517","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3686490.3686517","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 Signal Processing and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1569095176","https://openalex.org/W2169824909","https://openalex.org/W2194775991","https://openalex.org/W2500712151","https://openalex.org/W2565639579","https://openalex.org/W2761860076","https://openalex.org/W2810028092","https://openalex.org/W2875814315","https://openalex.org/W2944388686","https://openalex.org/W2955058313","https://openalex.org/W2963091558","https://openalex.org/W2963093690","https://openalex.org/W2963353821","https://openalex.org/W2963495494","https://openalex.org/W2963647456","https://openalex.org/W2963840241","https://openalex.org/W2963977642","https://openalex.org/W2964294787","https://openalex.org/W2967591398","https://openalex.org/W3103858256","https://openalex.org/W3163925244","https://openalex.org/W6600007113","https://openalex.org/W6600060591","https://openalex.org/W6600351811","https://openalex.org/W6600574797"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2772917594","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398","https://openalex.org/W2775347418"],"abstract_inverted_index":{"Segment-based":[0],"text":[1,7,24,95,123,138,156,175,180],"detection":[2,25,162,181],"methods":[3,190],"can":[4,176],"accurately":[5],"locate":[6],"regions":[8],"of":[9,34,38,53,70,140,150,164],"arbitrary":[10],"shapes,":[11],"but":[12],"there":[13],"are":[14,129],"two":[15],"problems":[16],"to":[17,66,84,145],"be":[18],"solved":[19],"in":[20,97,126],"the":[21,35,43,79,94,111,122,147,151,161,165,179],"current":[22],"segment-based":[23],"methods:":[26],"(1)":[27],"they":[28],"do":[29,47],"not":[30,48],"make":[31,67],"full":[32,68],"use":[33,69],"feature":[36,62,100,114,120,124,134,153],"maps":[37],"each":[39,127],"stage":[40],"output":[41],"by":[42,174],"backbone":[44],"network,":[45],"and":[46,158,183,196],"fully":[49],"extract":[50,85],"multi-scale":[51,61,86,112],"features":[52,87,96,139],"spatial":[54,90],"semantics;":[55],"(2)":[56],"There":[57],"is":[58,107],"no":[59],"suitable":[60],"fusion":[63,113],"module":[64],"designed":[65],"contextual":[71],"scale":[72,99],"information.":[73],"In":[74],"this":[75],"paper,":[76],"we":[77],"design":[78],"Spatial":[80],"Relationship":[81],"Module":[82,105],"(SPM)":[83],"with":[88,188],"powerful":[89],"semantics.":[91],"Aiming":[92],"at":[93],"different":[98,141],"maps,":[101,121],"a":[102],"Subspace":[103],"Attention":[104],"(SAM)":[106],"introduced.":[108],"By":[109],"dividing":[110],"map":[115,135,154],"channel":[116],"into":[117],"multiple":[118],"subspace":[119,128],"weights":[125],"learned":[130],"respectively.":[131],"The":[132,167],"fused":[133,152],"contains":[136],"more":[137],"scales,":[142],"so":[143],"as":[144],"enhance":[146],"representation":[148],"ability":[149],"for":[155],"instances,":[157],"then":[159],"improve":[160,178],"effect":[163],"network.":[166],"experimental":[168],"results":[169],"show":[170],"that":[171],"SPANet":[172],"proposed":[173],"greatly":[177],"capability,":[182],"has":[184],"excellent":[185],"performance":[186],"compared":[187],"other":[189],"on":[191],"public":[192],"data":[193],"sets":[194],"CTW1500":[195],"Total-text.":[197]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
