{"id":"https://openalex.org/W3089713139","doi":"https://doi.org/10.1109/icip40778.2020.9191102","title":"Scale-Invariant Multi-Oriented Text Detection in Wild Scene Image","display_name":"Scale-Invariant Multi-Oriented Text Detection in Wild Scene Image","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3089713139","doi":"https://doi.org/10.1109/icip40778.2020.9191102","mag":"3089713139"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9191102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9191102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5007429657","display_name":"Kinjal Dasgupta","orcid":"https://orcid.org/0000-0001-8488-3135"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kinjal Dasgupta","raw_affiliation_strings":["Heritage Institute of Technology, Kolkata"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heritage Institute of Technology, Kolkata","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100703255","display_name":"Sudip Das","orcid":"https://orcid.org/0000-0002-8372-4316"},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sudip Das","raw_affiliation_strings":["Indian Statistical Institute, Kolkata"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Statistical Institute, Kolkata","institution_ids":["https://openalex.org/I6498739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090127362","display_name":"Ujjwal Bhattacharya","orcid":"https://orcid.org/0000-0002-8546-6453"},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ujjwal Bhattacharya","raw_affiliation_strings":["Indian Statistical Institute, Kolkata"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Statistical Institute, Kolkata","institution_ids":["https://openalex.org/I6498739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"2041","last_page":"2045"},"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9940999746322632,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9908000230789185,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7343988418579102},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6964679956436157},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6334846615791321},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6159704923629761},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5799430012702942},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.5397011637687683},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4983527660369873},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4758817255496979},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4703291356563568},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4588821828365326},{"id":"https://openalex.org/keywords/text-detection","display_name":"Text detection","score":0.4580456614494324},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42449283599853516},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.42423728108406067},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17348966002464294}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7343988418579102},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6964679956436157},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6334846615791321},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6159704923629761},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5799430012702942},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.5397011637687683},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4983527660369873},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4758817255496979},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4703291356563568},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4588821828365326},{"id":"https://openalex.org/C2983589003","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Text detection","level":3,"score":0.4580456614494324},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42449283599853516},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.42423728108406067},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17348966002464294},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip40778.2020.9191102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9191102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W654550266","https://openalex.org/W1488125194","https://openalex.org/W1922126009","https://openalex.org/W1972065312","https://openalex.org/W2102605133","https://openalex.org/W2125556102","https://openalex.org/W2128854450","https://openalex.org/W2142159465","https://openalex.org/W2144554289","https://openalex.org/W2194775991","https://openalex.org/W2204328893","https://openalex.org/W2253806798","https://openalex.org/W2288122362","https://openalex.org/W2296073425","https://openalex.org/W2604735854","https://openalex.org/W2605982830","https://openalex.org/W2610691757","https://openalex.org/W2785383245","https://openalex.org/W2963056148","https://openalex.org/W2963398399","https://openalex.org/W2963840241","https://openalex.org/W2964018263","https://openalex.org/W2968226676","https://openalex.org/W2981969038","https://openalex.org/W2982007926","https://openalex.org/W3021927036","https://openalex.org/W6629021407","https://openalex.org/W6642972425","https://openalex.org/W6748033758"],"related_works":["https://openalex.org/W2045155990","https://openalex.org/W4313163053","https://openalex.org/W3045811229","https://openalex.org/W4300973204","https://openalex.org/W2085033728","https://openalex.org/W1483408780","https://openalex.org/W4285411112","https://openalex.org/W4284884309","https://openalex.org/W2908749798","https://openalex.org/W3011293149"],"abstract_inverted_index":{"Automatic":[0],"detection":[1,109],"of":[2,21,45,53,56,82,85,105,111],"scene":[3],"texts":[4,84],"in":[5,17,33,69,75],"the":[6,15,34,57,112,131],"wild":[7],"is":[8,80],"a":[9,41,46],"challenging":[10],"problem,":[11],"particularly":[12],"due":[13],"to":[14,67],"difficulties":[16,68],"handling":[18],"(i)":[19],"occlusions":[20,104],"varying":[22,106],"percentages,":[23],"(ii)":[24],"widely":[25],"different":[26,86],"scales":[27,87],"and":[28,72,88,126],"orientations,":[29],"(iii)":[30],"severe":[31],"degradation":[32],"image":[35,70],"quality":[36],"etc.":[37],"Here,":[38],"we":[39,61],"propose":[40],"deep":[42],"architecture":[43],"consisting":[44],"novel":[47],"Feature":[48],"Representation":[49],"Block":[50],"(FRB)":[51],"capable":[52,81],"efficient":[54],"abstraction":[55],"input":[58],"information.":[59],"Also,":[60],"consider":[62],"curriculum":[63],"learning":[64],"with":[65],"respect":[66],"samples":[71],"gradual":[73],"increase":[74],"pixel-wise":[76],"blurring.":[77],"Our":[78],"framework":[79,114],"detecting":[83],"orientations.":[89],"It":[90],"can":[91],"tackle":[92],"blurring":[93],"from":[94],"multiple":[95],"possible":[96],"sources,":[97],"non-uniform":[98],"illumination":[99],"as":[100,102],"well":[101],"partial":[103],"percentages.":[107],"Text":[108],"performance":[110],"proposed":[113],"on":[115],"various":[116],"benchmark":[117],"datasets":[118],"including":[119],"ICDAR":[120,122],"2015,":[121],"2017":[123],"MLT,":[124],"MSRA-TD500":[125],"COCO-Text":[127],"has":[128],"significantly":[129],"improved":[130],"respective":[132],"state-of-the-art":[133],"results.":[134]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}