{"id":"https://openalex.org/W7154724451","doi":"https://doi.org/10.1007/s10032-026-00585-7","title":"U-DIADS-TL: a novel dataset for text line segmentation in historical manuscripts","display_name":"U-DIADS-TL: a novel dataset for text line segmentation in historical manuscripts","publication_year":2026,"publication_date":"2026-04-18","ids":{"openalex":"https://openalex.org/W7154724451","doi":"https://doi.org/10.1007/s10032-026-00585-7"},"language":"en","primary_location":{"id":"doi:10.1007/s10032-026-00585-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10032-026-00585-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10032-026-00585-7.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/s10032-026-00585-7.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006225173","display_name":"Silvia Zottin","orcid":"https://orcid.org/0000-0003-0820-7260"},"institutions":[{"id":"https://openalex.org/I129043915","display_name":"University of Udine","ror":"https://ror.org/05ht0mh31","country_code":"IT","type":"education","lineage":["https://openalex.org/I129043915"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Silvia Zottin","raw_affiliation_strings":["AVML Lab, Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AVML Lab, Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy","institution_ids":["https://openalex.org/I129043915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079038522","display_name":"Axel De Nardin","orcid":"https://orcid.org/0000-0002-0762-708X"},"institutions":[{"id":"https://openalex.org/I129043915","display_name":"University of Udine","ror":"https://ror.org/05ht0mh31","country_code":"IT","type":"education","lineage":["https://openalex.org/I129043915"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Axel De Nardin","raw_affiliation_strings":["AVML Lab, Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AVML Lab, Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy","institution_ids":["https://openalex.org/I129043915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004078798","display_name":"Claudio Piciarelli","orcid":"https://orcid.org/0000-0001-5305-1520"},"institutions":[{"id":"https://openalex.org/I129043915","display_name":"University of Udine","ror":"https://ror.org/05ht0mh31","country_code":"IT","type":"education","lineage":["https://openalex.org/I129043915"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Claudio Piciarelli","raw_affiliation_strings":["AVML Lab, Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AVML Lab, Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy","institution_ids":["https://openalex.org/I129043915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078277002","display_name":"Gian Luca Foresti","orcid":"https://orcid.org/0000-0002-8425-6892"},"institutions":[{"id":"https://openalex.org/I129043915","display_name":"University of Udine","ror":"https://ror.org/05ht0mh31","country_code":"IT","type":"education","lineage":["https://openalex.org/I129043915"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Gian Luca Foresti","raw_affiliation_strings":["AVML Lab, Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AVML Lab, Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy","institution_ids":["https://openalex.org/I129043915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5006225173"],"corresponding_institution_ids":["https://openalex.org/I129043915"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.48635493,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":"2","first_page":"647","last_page":"658"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9936000108718872,"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":0.9936000108718872,"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/T10028","display_name":"Topic Modeling","score":0.0006000000284984708,"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/T12377","display_name":"Digital Humanities and Scholarship","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1208","display_name":"Literature and Literary Theory"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7117000222206116},{"id":"https://openalex.org/keywords/historical-document","display_name":"Historical document","score":0.6686000227928162},{"id":"https://openalex.org/keywords/handwriting","display_name":"Handwriting","score":0.554099977016449},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.477400004863739},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"Text segmentation","score":0.40689998865127563},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.40220001339912415},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.39579999446868896},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.37369999289512634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7398999929428101},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7117000222206116},{"id":"https://openalex.org/C2778371909","wikidata":"https://www.wikidata.org/wiki/Q3771738","display_name":"Historical document","level":2,"score":0.6686000227928162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6001999974250793},{"id":"https://openalex.org/C2779386606","wikidata":"https://www.wikidata.org/wiki/Q2393642","display_name":"Handwriting","level":2,"score":0.554099977016449},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.477400004863739},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4415000081062317},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.40689998865127563},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.37040001153945923},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.35580000281333923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31360000371932983},{"id":"https://openalex.org/C2983812711","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Text recognition","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C72773152","wikidata":"https://www.wikidata.org/wiki/Q5287629","display_name":"Document layout analysis","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2630000114440918}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10032-026-00585-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10032-026-00585-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10032-026-00585-7.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"},{"id":"pmh:oai:air.uniud.it:11390/1330690","is_oa":true,"landing_page_url":"https://hdl.handle.net/11390/1330690","pdf_url":null,"source":{"id":"https://openalex.org/S4306401163","display_name":"Institutional Research Information System (University of Udine)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I129043915","host_organization_name":"University of Udine","host_organization_lineage":["https://openalex.org/I129043915"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s10032-026-00585-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10032-026-00585-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10032-026-00585-7.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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7154724451.pdf","grobid_xml":"https://content.openalex.org/works/W7154724451.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W2012795462","https://openalex.org/W2102313619","https://openalex.org/W2109833833","https://openalex.org/W2128060444","https://openalex.org/W2165853248","https://openalex.org/W2560023338","https://openalex.org/W2576852589","https://openalex.org/W2905836308","https://openalex.org/W2906024304","https://openalex.org/W2964309882","https://openalex.org/W2986223646","https://openalex.org/W3000820403","https://openalex.org/W3003426521","https://openalex.org/W3004115788","https://openalex.org/W3107428334","https://openalex.org/W3133924922","https://openalex.org/W3134564617","https://openalex.org/W3161970575","https://openalex.org/W3210946864","https://openalex.org/W4288749659","https://openalex.org/W4296849103","https://openalex.org/W4319301068","https://openalex.org/W4385350719","https://openalex.org/W4390917550","https://openalex.org/W4391044752","https://openalex.org/W4394625806","https://openalex.org/W4401691171","https://openalex.org/W4402423507","https://openalex.org/W4414192413"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Text":[1,85],"line":[2,93],"segmentation":[3,94,129,157],"in":[4,95],"historical":[5,64,148],"documents":[6],"remains":[7],"a":[8,49,87,141],"significant":[9],"challenge":[10],"due":[11],"to":[12,42,63,127],"degraded":[13],"manuscripts,":[14],"complex":[15],"layouts,":[16],"and":[17,39,106,147],"diverse":[18,108],"handwriting":[19],"styles.":[20],"Developing":[21],"robust":[22],"computational":[23],"methods":[24],"is":[25,67],"hindered":[26],"by":[27,52],"the":[28,152],"scarcity":[29],"of":[30,154],"high-quality":[31],"ground":[32],"truth":[33],"annotations,":[34],"which":[35],"require":[36],"expert":[37],"knowledge":[38],"are":[40],"time-intensive":[41],"produce.":[43],"Few-shot":[44],"learning":[45,117,146],"has":[46],"emerged":[47],"as":[48,140],"promising":[50],"solution":[51],"enabling":[53],"model":[54],"training":[55,123],"with":[56,102],"minimal":[57],"annotated":[58],"data,":[59],"yet":[60],"its":[61],"application":[62],"document":[65,109,149],"analysis":[66],"still":[68],"largely":[69],"unexplored.":[70],"To":[71,114],"address":[72],"this":[73],"limitation,":[74],"we":[75,119],"introduce":[76],"U-DIADS-TL":[77,98],"(Uniud":[78],"-":[79,84],"Document":[80],"Image":[81],"Analysis":[82],"DataSet":[83],"Line),":[86],"dataset":[88,138],"specifically":[89],"designed":[90],"for":[91,159],"text":[92,104],"ancient":[96],"manuscripts.":[97],"provides":[99],"noise-free":[100],"annotations":[101],"non-overlapping":[103],"elements":[105],"accommodates":[107],"structures,":[110],"including":[111],"multi-column":[112],"layouts.":[113],"encourage":[115],"few-shot":[116],"approaches,":[118],"offer":[120],"only":[121],"three":[122],"images,":[124],"allowing":[125],"researchers":[126],"develop":[128],"models":[130,158],"that":[131],"can":[132],"generalize":[133],"from":[134],"limited":[135],"supervision.":[136],"Our":[137],"serves":[139],"critical":[142],"bridge":[143],"between":[144],"deep":[145],"analysis,":[150],"fostering":[151],"creation":[153],"efficient,":[155],"adaptable":[156],"real-world":[160],"applications.":[161]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-04-18T00:00:00"}
