{"id":"https://openalex.org/W3194722168","doi":"https://doi.org/10.1109/tip.2021.3125260","title":"Stroke-Based Scene Text Erasing Using Synthetic Data for Training","display_name":"Stroke-Based Scene Text Erasing Using Synthetic Data for Training","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3194722168","doi":"https://doi.org/10.1109/tip.2021.3125260","mag":"3194722168","pmid":"https://pubmed.ncbi.nlm.nih.gov/34752394"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2021.3125260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3125260","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2104.11493","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017897073","display_name":"Zhengmi Tang","orcid":"https://orcid.org/0000-0003-2011-8105"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhengmi Tang","raw_affiliation_strings":["Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan. (e-mail: tzm@dc.tohoku.ac.jp)","Tohoku University (),"],"raw_orcid":"https://orcid.org/0000-0003-2011-8105","affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan. (e-mail: tzm@dc.tohoku.ac.jp)","institution_ids":["https://openalex.org/I201537933"]},{"raw_affiliation_string":"Tohoku University (),","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009192524","display_name":"Tomo Miyazaki","orcid":"https://orcid.org/0000-0001-5205-0542"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomo Miyazaki","raw_affiliation_strings":["Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan","Tohoku University (),"],"raw_orcid":"https://orcid.org/0000-0001-5205-0542","affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan","institution_ids":["https://openalex.org/I201537933"]},{"raw_affiliation_string":"Tohoku University (),","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029206786","display_name":"Yoshihiro Sugaya","orcid":"https://orcid.org/0000-0003-3704-4309"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshihiro Sugaya","raw_affiliation_strings":["Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan","Tohoku University (),"],"raw_orcid":"https://orcid.org/0000-0003-3704-4309","affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan","institution_ids":["https://openalex.org/I201537933"]},{"raw_affiliation_string":"Tohoku University (),","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020830042","display_name":"Shinichiro Omachi","orcid":"https://orcid.org/0000-0001-7706-9995"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shinichiro Omachi","raw_affiliation_strings":["Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan","Tohoku University (),"],"raw_orcid":"https://orcid.org/0000-0001-7706-9995","affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan","institution_ids":["https://openalex.org/I201537933"]},{"raw_affiliation_string":"Tohoku University (),","institution_ids":["https://openalex.org/I201537933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I201537933"],"apc_list":null,"apc_paid":null,"fwci":0.0908,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38606528,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"30","issue":null,"first_page":"9306","last_page":"9320"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9970999956130981,"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":0.9970999956130981,"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":0.996999979019165,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9916999936103821,"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/inpainting","display_name":"Inpainting","score":0.7635747194290161},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7624363303184509},{"id":"https://openalex.org/keywords/text-detection","display_name":"Text detection","score":0.6965537071228027},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6726488471031189},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.642388641834259},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6336710453033447},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.597088098526001},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5059458613395691},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4657014012336731},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45719635486602783},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41129374504089355},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3965500593185425},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3314833641052246},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.09515851736068726}],"concepts":[{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.7635747194290161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7624363303184509},{"id":"https://openalex.org/C2983589003","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Text detection","level":3,"score":0.6965537071228027},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6726488471031189},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.642388641834259},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6336710453033447},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.597088098526001},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5059458613395691},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4657014012336731},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45719635486602783},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41129374504089355},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3965500593185425},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3314833641052246},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.09515851736068726},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/tip.2021.3125260","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3125260","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:34752394","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34752394","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:2104.11493","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.11493","pdf_url":"https://arxiv.org/pdf/2104.11493","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3194722168","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2104.11493.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2104.11493","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2104.11493","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2104.11493","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.11493","pdf_url":"https://arxiv.org/pdf/2104.11493","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3015985035","display_name":"Challenge to vision technology without cameras","funder_award_id":"18K19772","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G3305153135","display_name":"Development of Technology for Realizing High-Compression of Video by Extracting Important Regions","funder_award_id":"20H04201","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G6924808611","display_name":"Development of Parametric Image Generation Model for Machine Learning","funder_award_id":"19K12033","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":78,"referenced_works":["https://openalex.org/W1488125194","https://openalex.org/W1521064364","https://openalex.org/W1975049209","https://openalex.org/W1976597276","https://openalex.org/W1986119343","https://openalex.org/W1993120651","https://openalex.org/W1999360130","https://openalex.org/W1999885501","https://openalex.org/W2008806374","https://openalex.org/W2055132753","https://openalex.org/W2082269761","https://openalex.org/W2117539524","https://openalex.org/W2123931155","https://openalex.org/W2128854450","https://openalex.org/W2131673214","https://openalex.org/W2133665775","https://openalex.org/W2138715174","https://openalex.org/W2144554289","https://openalex.org/W2150823190","https://openalex.org/W2160197540","https://openalex.org/W2194775991","https://openalex.org/W2253806798","https://openalex.org/W2295936755","https://openalex.org/W2331128040","https://openalex.org/W2339589954","https://openalex.org/W2343052201","https://openalex.org/W2475287302","https://openalex.org/W2519818067","https://openalex.org/W2593539516","https://openalex.org/W2605982830","https://openalex.org/W2612775908","https://openalex.org/W2613718673","https://openalex.org/W2732026016","https://openalex.org/W2738588019","https://openalex.org/W2785383245","https://openalex.org/W2798365772","https://openalex.org/W2810028092","https://openalex.org/W2875814315","https://openalex.org/W2962773189","https://openalex.org/W2962835968","https://openalex.org/W2962914239","https://openalex.org/W2963005009","https://openalex.org/W2963073614","https://openalex.org/W2963150697","https://openalex.org/W2963255313","https://openalex.org/W2963353821","https://openalex.org/W2963420272","https://openalex.org/W2963879280","https://openalex.org/W2964121744","https://openalex.org/W2967615747","https://openalex.org/W2982220924","https://openalex.org/W2982763192","https://openalex.org/W2985764327","https://openalex.org/W2995942257","https://openalex.org/W2998584079","https://openalex.org/W2998621280","https://openalex.org/W3003433862","https://openalex.org/W3003868038","https://openalex.org/W3003921261","https://openalex.org/W3010428518","https://openalex.org/W3034419329","https://openalex.org/W3035512475","https://openalex.org/W3043547428","https://openalex.org/W3082245115","https://openalex.org/W3100936573","https://openalex.org/W3106228955","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6679774445","https://openalex.org/W6682043285","https://openalex.org/W6691603626","https://openalex.org/W6702130928","https://openalex.org/W6729791593","https://openalex.org/W6753471521","https://openalex.org/W6785652829","https://openalex.org/W6786010160"],"related_works":["https://openalex.org/W3213555225","https://openalex.org/W3157231307","https://openalex.org/W3185271132","https://openalex.org/W3008655576","https://openalex.org/W2464918637","https://openalex.org/W2772406708","https://openalex.org/W3104052051","https://openalex.org/W3174746398","https://openalex.org/W3003433862","https://openalex.org/W2891451370","https://openalex.org/W3003191517","https://openalex.org/W2798957492","https://openalex.org/W2945674343","https://openalex.org/W2988536940","https://openalex.org/W284125591","https://openalex.org/W2591579733","https://openalex.org/W3005168914","https://openalex.org/W2787131894","https://openalex.org/W3204678987","https://openalex.org/W2962810613"],"abstract_inverted_index":{"Scene":[0],"text":[1,5,32,34,82,100,119,129,145,165],"erasing,":[2],"which":[3],"replaces":[4],"regions":[6],"with":[7,151,157],"reasonable":[8],"content":[9,135],"in":[10,17,22,30,147],"natural":[11],"images,":[12],"has":[13],"drawn":[14],"significant":[15],"attention":[16],"the":[18,49,70,93,97,118,127,171,177],"computer":[19],"vision":[20],"community":[21],"recent":[23],"years.":[24],"There":[25],"are":[26,195],"two":[27],"potential":[28],"subtasks":[29,40],"scene":[31,149,164],"erasing:":[33],"detection":[35],"and":[36,86,111,173,180],"image":[37,130,150],"inpainting.":[38],"Both":[39],"require":[41],"considerable":[42,78],"data":[43],"to":[44,63,131],"achieve":[45],"better":[46,137],"performance;":[47],"however,":[48],"lack":[50,71],"of":[51,72,80],"a":[52,106,122,148,152],"large-scale":[53],"real-world":[54,74,198],"scene-text":[55,160],"removal":[56],"dataset":[57,94],"does":[58],"not":[59],"allow":[60],"existing":[61,159,189],"methods":[62,191],"realize":[64],"their":[65],"potential.":[66],"To":[67],"compensate":[68],"for":[69,136,162],"pairwise":[73],"data,":[75],"we":[76],"made":[77],"use":[79],"synthetic":[81,99],"after":[83],"additional":[84],"enhancement":[85],"subsequently":[87],"trained":[88,196],"our":[89,185],"model":[90,141],"only":[91],"on":[92,176,197],"generated":[95],"by":[96],"improved":[98],"engine.":[101],"Our":[102],"proposed":[103],"network":[104],"contains":[105],"stroke":[107,120],"mask":[108],"prediction":[109],"module":[110,114],"background":[112,134],"inpainting":[113,138],"that":[115,184],"can":[116,142],"extract":[117],"as":[121],"relatively":[123],"small":[124],"hole":[125],"from":[126,170],"cropped":[128],"maintain":[132],"more":[133],"results.":[139],"This":[140],"partially":[143],"erase":[144],"instances":[146],"bounding":[153],"box":[154],"or":[155],"work":[156],"an":[158],"detector":[161],"automatic":[163],"erasing.":[166],"The":[167],"experimental":[168],"results":[169],"qualitative":[172],"quantitative":[174],"evaluation":[175],"SCUT-Syn,":[178],"ICDAR2013,":[179],"SCUT-EnsText":[181],"datasets":[182],"demonstrate":[183],"method":[186],"significantly":[187],"outperforms":[188],"state-of-the-art":[190],"even":[192],"when":[193],"they":[194],"data.":[199]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
