{"id":"https://openalex.org/W4399389699","doi":"https://doi.org/10.1007/s10032-024-00477-8","title":"Neural models for semantic analysis of handwritten document images","display_name":"Neural models for semantic analysis of handwritten document images","publication_year":2024,"publication_date":"2024-06-06","ids":{"openalex":"https://openalex.org/W4399389699","doi":"https://doi.org/10.1007/s10032-024-00477-8"},"language":"en","primary_location":{"id":"doi:10.1007/s10032-024-00477-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10032-024-00477-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10032-024-00477-8.pdf","source":{"id":"https://openalex.org/S90108747","display_name":"International Journal on Document Analysis and Recognition (IJDAR)","issn_l":"1433-2825","issn":["1433-2825","1433-2833"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal on Document Analysis and Recognition (IJDAR)","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10032-024-00477-8.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046695777","display_name":"Oliver T\u00fcselmann","orcid":"https://orcid.org/0000-0002-8892-3306"},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]},{"id":"https://openalex.org/I4387154616","display_name":"Lamarr Institute for Machine Learning and Artificial Intelligence","ror":"https://ror.org/04s11ea33","country_code":null,"type":"facility","lineage":["https://openalex.org/I135140700","https://openalex.org/I200332995","https://openalex.org/I4210098062","https://openalex.org/I4210144576","https://openalex.org/I4387154616","https://openalex.org/I4923324","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Oliver T\u00fcselmann","raw_affiliation_strings":["Department of Computer Science, TU Dortmund University, Dortmund, Germany","Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]},{"raw_affiliation_string":"Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany","institution_ids":["https://openalex.org/I4387154616"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048407937","display_name":"Gernot A. Fink","orcid":"https://orcid.org/0000-0002-7446-7813"},"institutions":[{"id":"https://openalex.org/I4387154616","display_name":"Lamarr Institute for Machine Learning and Artificial Intelligence","ror":"https://ror.org/04s11ea33","country_code":null,"type":"facility","lineage":["https://openalex.org/I135140700","https://openalex.org/I200332995","https://openalex.org/I4210098062","https://openalex.org/I4210144576","https://openalex.org/I4387154616","https://openalex.org/I4923324","https://openalex.org/I4923324"]},{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gernot A. Fink","raw_affiliation_strings":["Department of Computer Science, TU Dortmund University, Dortmund, Germany","Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]},{"raw_affiliation_string":"Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany","institution_ids":["https://openalex.org/I4387154616"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5046695777"],"corresponding_institution_ids":["https://openalex.org/I200332995","https://openalex.org/I4387154616"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.4584,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60683271,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"27","issue":"3","first_page":"245","last_page":"263"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9990000128746033,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9790999889373779,"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":0.8544305562973022},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6168283224105835},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5496271848678589},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.48627960681915283}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8544305562973022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6168283224105835},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5496271848678589},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.48627960681915283}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10032-024-00477-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10032-024-00477-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10032-024-00477-8.pdf","source":{"id":"https://openalex.org/S90108747","display_name":"International Journal on Document Analysis and Recognition (IJDAR)","issn_l":"1433-2825","issn":["1433-2825","1433-2833"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal on Document Analysis and Recognition (IJDAR)","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10032-024-00477-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10032-024-00477-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10032-024-00477-8.pdf","source":{"id":"https://openalex.org/S90108747","display_name":"International Journal on Document Analysis and Recognition (IJDAR)","issn_l":"1433-2825","issn":["1433-2825","1433-2833"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal on Document Analysis and Recognition (IJDAR)","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4399999976158142,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320329559","display_name":"Technische Universit\u00e4t Dortmund","ror":"https://ror.org/01k97gp34"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4399389699.pdf"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W2053317383","https://openalex.org/W2076956527","https://openalex.org/W2109495831","https://openalex.org/W2152928267","https://openalex.org/W2194775991","https://openalex.org/W2442408233","https://openalex.org/W2493916176","https://openalex.org/W2594443378","https://openalex.org/W2625800120","https://openalex.org/W2785887616","https://openalex.org/W2787247660","https://openalex.org/W2889198729","https://openalex.org/W2905867583","https://openalex.org/W2913752175","https://openalex.org/W2962739339","https://openalex.org/W2963271566","https://openalex.org/W2963748441","https://openalex.org/W2963908984","https://openalex.org/W2964138343","https://openalex.org/W2988217457","https://openalex.org/W3002072934","https://openalex.org/W3003784944","https://openalex.org/W3003958013","https://openalex.org/W3012289950","https://openalex.org/W3021347667","https://openalex.org/W3034368386","https://openalex.org/W3040983797","https://openalex.org/W3083876082","https://openalex.org/W3095789240","https://openalex.org/W3104415840","https://openalex.org/W3107880884","https://openalex.org/W3120043490","https://openalex.org/W3138154797","https://openalex.org/W3176664887","https://openalex.org/W3176851559","https://openalex.org/W3195780241","https://openalex.org/W3197857628","https://openalex.org/W3198525710","https://openalex.org/W3199662997","https://openalex.org/W3202367703","https://openalex.org/W3202839357","https://openalex.org/W3211950664","https://openalex.org/W4205137784","https://openalex.org/W4205894758","https://openalex.org/W4213009331","https://openalex.org/W4230543443","https://openalex.org/W4238410424","https://openalex.org/W4241698512","https://openalex.org/W4281665026","https://openalex.org/W4285172022","https://openalex.org/W4285206788","https://openalex.org/W4312233877","https://openalex.org/W4312302504","https://openalex.org/W4312475622","https://openalex.org/W4312649342","https://openalex.org/W4318336633","https://openalex.org/W4320481960","https://openalex.org/W4385982258","https://openalex.org/W4385990901","https://openalex.org/W4385991039","https://openalex.org/W4385991941"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2033914206","https://openalex.org/W2042327336","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Abstract":[0],"Semantic":[1],"analysis":[2],"of":[3,11,18,45,91,117,156,173,188],"handwritten":[4,19,119],"document":[5,120],"images":[6],"offers":[7,30],"a":[8,24,115,136,154,186],"wide":[9],"range":[10],"practical":[12],"application":[13],"scenarios.":[14],"A":[15,69],"sequential":[16],"combination":[17],"text":[20,54],"recognition":[21,55],"(HTR)":[22],"and":[23,56,107,111,130],"task-specific":[25],"natural":[26],"language":[27],"processing":[28],"system":[29],"an":[31,105,108],"intuitive":[32],"solution":[33],"in":[34,96,181],"this":[35,38,62,73,101],"domain.":[36,99],"However,":[37],"HTR-based":[39,106],"approach":[40,140],"suffers":[41],"from":[42,145],"the":[43,58,92,97,171],"problem":[44],"error":[46],"propagation.":[47],"An":[48],"HTR-free":[49,109,151,179],"model,":[50],"which":[51,85],"avoids":[52],"explicit":[53],"solves":[57],"task":[59],"end-to-end,":[60],"tackles":[61],"problem,":[63],"but":[64],"often":[65],"produces":[66],"poor":[67],"results.":[68],"possible":[70],"reason":[71],"for":[72,162,178],"is":[74,176],"that":[75,170],"it":[76],"does":[77],"not":[78],"incorporate":[79],"largely":[80],"pre-trained":[81,147],"semantic":[82,124,143,164,174],"word":[83,125,148,165],"embeddings,":[84],"turn":[86],"out":[87],"to":[88,141],"be":[89],"one":[90],"most":[93],"powerful":[94],"advantages":[95],"textual":[98],"In":[100,153],"work,":[102],"we":[103,134,158],"propose":[104,135],"model":[110],"compare":[112],"them":[113],"on":[114,185],"variety":[116,187],"segmentation-based":[118],"image":[121,166],"benchmarks":[122],"including":[123],"spotting,":[126],"named":[127],"entity":[128],"recognition,":[129],"question":[131],"answering.":[132],"Furthermore,":[133],"cross-modal":[137],"knowledge":[138,144,175],"distillation":[139],"integrate":[142],"textually":[146],"embeddings":[149],"into":[150],"models.":[152],"series":[155],"experiments,":[157],"investigate":[159],"optimization":[160],"strategies":[161],"robust":[163],"representation.":[167],"We":[168],"show":[169],"incorporation":[172],"beneficial":[177],"approaches":[180],"achieving":[182],"state-of-the-art":[183],"results":[184],"benchmarks.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-09T13:55:54.758798","created_date":"2025-10-10T00:00:00"}