{"id":"https://openalex.org/W4413926194","doi":"https://doi.org/10.1109/tmm.2025.3604892","title":"On the Behavior of Contrastive Regularization in Improving Chinese Text Recognizer","display_name":"On the Behavior of Contrastive Regularization in Improving Chinese Text Recognizer","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413926194","doi":"https://doi.org/10.1109/tmm.2025.3604892"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2025.3604892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3604892","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/A5013971053","display_name":"Dekang Liu","orcid":"https://orcid.org/0000-0003-1630-5928"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dekang Liu","raw_affiliation_strings":["Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China","Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0003-1630-5928","affiliations":[{"raw_affiliation_string":"Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]},{"raw_affiliation_string":"Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075474564","display_name":"Tianlei Wang","orcid":"https://orcid.org/0000-0002-4498-4326"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianlei Wang","raw_affiliation_strings":["Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China","Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-4498-4326","affiliations":[{"raw_affiliation_string":"Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]},{"raw_affiliation_string":"Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035422552","display_name":"Huanqiang Zeng","orcid":"https://orcid.org/0000-0002-2802-7745"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanqiang Zeng","raw_affiliation_strings":["School of Information Science and Engineering, Huaqiao University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-2802-7745","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Huaqiao University, Xiamen, China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074063024","display_name":"Jiuwen Cao","orcid":"https://orcid.org/0000-0002-6480-5794"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiuwen Cao","raw_affiliation_strings":["Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China","Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-6480-5794","affiliations":[{"raw_affiliation_string":"Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]},{"raw_affiliation_string":"Machine Learning and I-health International Cooperation Base of Zhejiang Province, and Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.396,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85714575,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"27","issue":null,"first_page":"8068","last_page":"8080"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9340999722480774,"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"}},"topics":[{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9340999722480774,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9244999885559082,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.920199990272522,"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":0.856893002986908},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5313573479652405},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5028976798057556},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4932860732078552},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.38984912633895874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.856893002986908},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5313573479652405},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5028976798057556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4932860732078552},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.38984912633895874}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2025.3604892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2025.3604892","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/G7151297541","display_name":null,"funder_award_id":"U1909209","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/W1974758710","https://openalex.org/W2049951199","https://openalex.org/W2144554289","https://openalex.org/W2194187530","https://openalex.org/W2194775991","https://openalex.org/W2810983211","https://openalex.org/W2963526661","https://openalex.org/W2965066169","https://openalex.org/W2978036638","https://openalex.org/W3034447740","https://openalex.org/W3035106683","https://openalex.org/W3035524453","https://openalex.org/W3175593095","https://openalex.org/W3175855397","https://openalex.org/W3177505580","https://openalex.org/W3181186176","https://openalex.org/W3217697975","https://openalex.org/W4229030834","https://openalex.org/W4283805255","https://openalex.org/W4283819468","https://openalex.org/W4283821822","https://openalex.org/W4292825881","https://openalex.org/W4310335763","https://openalex.org/W4312298460","https://openalex.org/W4312787220","https://openalex.org/W4313045695","https://openalex.org/W4321609266","https://openalex.org/W4385245566","https://openalex.org/W4385768208","https://openalex.org/W4389665150","https://openalex.org/W4390463734","https://openalex.org/W4390871832","https://openalex.org/W4390871889","https://openalex.org/W4390873548","https://openalex.org/W4396877736","https://openalex.org/W4402702988","https://openalex.org/W4403780826","https://openalex.org/W4405399726"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"The":[0,130],"dense":[1],"representation":[2,145],"space":[3],"in":[4,164,203],"Chinese":[5,190,222],"scene":[6],"text":[7],"recognition":[8,128,205],"(STR)":[9],"makes":[10],"discriminating":[11],"between":[12,46,62],"categories":[13],"highly":[14],"challenging,":[15],"because":[16],"of":[17,53,181,218],"the":[18,44,50,59,91,97,135,144,179,200,221],"large":[19],"candidate":[20],"category":[21],"set.":[22],"Mainstream":[23],"STR":[24,79,169,191],"methods":[25],"have":[26],"achieved":[27],"remarkable":[28],"advancements":[29],"by":[30,43],"leveraging":[31],"linguistic":[32],"knowledge":[33],"to":[34,74,81],"implicitly":[35],"address":[36],"this":[37,40,63,83],"challenge.":[38],"In":[39],"paper,":[41],"inspired":[42],"correlation":[45,64],"recognizer":[47],"performance":[48,92,206],"and":[49,65,85,104,116,126,141,147,153,167,195],"distributional":[51,107],"properties":[52,108],"character":[54],"representations,":[55],"as":[56,58,193],"well":[57],"inherent":[60],"consistency":[61],"supervised":[66],"contrastive":[67],"learning":[68],"(SupCon),":[69],"we":[70,95],"thoroughly":[71],"investigate":[72],"how":[73,134],"integrate":[75],"SupCon":[76],"with":[77,101,198],"an":[78],"model":[80],"alleviate":[82],"challenge,":[84],"elucidate":[86],"some":[87],"dynamic":[88],"behaviors":[89],"underlying":[90],"improvements.":[93],"Specifically,":[94],"analyze":[96],"SupCon-STR":[98,182,197],"models":[99,192],"instantiated":[100],"different":[102],"projectors":[103,142],"evaluate":[105],"their":[106],"through":[109],"metrics,":[110],"including":[111],"intra-class":[112,151],"compactness,":[113],"inter-class":[114,155],"separability,":[115],"feature":[117],"redundancy,":[118],"while":[119],"assessing":[120],"performances":[121,163],"that":[122,149],"involve":[123],"in-domain":[124,166],"accuracy":[125,217],"cross-domain":[127,168,204],"generalization.":[129],"main":[131],"results":[132,173],"reveal":[133],"temperature":[136],"<inline-formula":[137],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[138,224],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math":[139],"notation=\"LaTeX\">$\\tau$</tex-math></inline-formula>":[140],"affect":[143],"distribution,":[146],"highlight":[148],"suitable":[150],"compactness":[152],"sufficient":[154],"separability":[156],"are":[157,207],"key":[158],"factors":[159],"for":[160,184],"delivering":[161],"competitive":[162],"both":[165],"scenarios.":[170],"Moreover,":[171],"these":[172],"also":[174,228],"provide":[175],"valuable":[176],"insights":[177],"into":[178],"design":[180],"architectures":[183],"diverse":[185],"resource":[186],"constraints.":[187],"Taking":[188],"existing":[189],"baselines,":[194],"combining":[196],"them,":[199],"average":[201],"improvements":[202],"over":[208],"5%":[209],"across":[210],"7":[211],"testing":[212],"datasets.":[213],"A":[214],"new":[215],"state-of-the-art":[216],"77.19%":[219],"on":[220],"<italic":[223],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Scene</i>":[225],"benchmark":[226],"is":[227],"established.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-14T23:14:49.485078","created_date":"2025-10-10T00:00:00"}