{"id":"https://openalex.org/W7165687032","doi":"https://doi.org/10.48550/arxiv.2606.22608","title":"Automated sign detection across the Electronic Babylonian Library: A large-scale dataset and end-to-end cuneiform OCR pipeline","display_name":"Automated sign detection across the Electronic Babylonian Library: A large-scale dataset and end-to-end cuneiform OCR pipeline","publication_year":2026,"publication_date":"2026-06-21","ids":{"openalex":"https://openalex.org/W7165687032","doi":"https://doi.org/10.48550/arxiv.2606.22608"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22608","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22608","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.22608","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139198783","display_name":"Wentao Che","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Che, Wentao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114076222","display_name":"Esteban Garces Arias","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arias, Esteban Garc\u00e9s","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090673358","display_name":"Asim Niaz","orcid":"https://orcid.org/0000-0003-3905-9774"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niaz, Asim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139189667","display_name":"Andreas Bender","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bender, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139140319","display_name":"Enrique Jim\u00e9nez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jim\u00e9nez, Enrique","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.7077000141143799,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.7077000141143799,"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/T12307","display_name":"Ancient Near East History","score":0.15919999778270721,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14180","display_name":"Law, logistics, and international trade","score":0.009999999776482582,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5325999855995178},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.49970000982284546},{"id":"https://openalex.org/keywords/decipherment","display_name":"Decipherment","score":0.49900001287460327},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4602000117301941},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42489999532699585},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.383899986743927},{"id":"https://openalex.org/keywords/optical-character-recognition","display_name":"Optical character recognition","score":0.36309999227523804},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.3564000129699707},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.3248000144958496}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7296000123023987},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.63919997215271},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5325999855995178},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C2778467380","wikidata":"https://www.wikidata.org/wiki/Q1345443","display_name":"Decipherment","level":2,"score":0.49900001287460327},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4602000117301941},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45210000872612},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42489999532699585},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3634999990463257},{"id":"https://openalex.org/C546480517","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Optical character recognition","level":3,"score":0.36309999227523804},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3248000144958496},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C157511935","wikidata":"https://www.wikidata.org/wiki/Q401","display_name":"Cuneiform","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.2955000102519989},{"id":"https://openalex.org/C2779308522","wikidata":"https://www.wikidata.org/wiki/Q843958","display_name":"Digitization","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.29339998960494995},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.2818000018596649},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.27570000290870667},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25600001215934753},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22608","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22608","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.22608","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22608","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6007117629051208,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning":[0],"to":[1,51,90,104,119,146],"read":[2],"cuneiform":[3,48,161],"tablets":[4],"is":[5,53,64,117],"an":[6],"extremely":[7],"demanding":[8],"task;":[9],"consequently,":[10],"of":[11,70,102],"the":[12,45,115,124,137],"roughly":[13],"half":[14],"million":[15,133],"excavated":[16],"tablets,":[17],"only":[18],"a":[19,30,56,154],"small":[20],"fraction":[21],"has":[22],"been":[23],"analysed":[24],"by":[25],"Assyriologists.":[26],"Computer":[27],"vision":[28],"offers":[29],"promising":[31],"avenue":[32],"for":[33,159],"decipherment":[34],"but":[35],"requires":[36],"large,":[37],"densely":[38],"annotated":[39,47],"datasets.":[40],"To":[41],"address":[42],"this":[43],"limitation,":[44],"largest":[46],"sign":[49,93,134],"dataset":[50],"date":[52],"used,":[54],"and":[55,72,85,95,98,143,149,156,163,169],"Deformable":[57],"Detection":[58],"Transformer":[59],"(DETR)-based":[60],"object":[61],"detection":[62,94,111],"model":[63],"evaluated":[65],"under":[66],"two":[67],"class":[68],"granularities":[69],"173":[71],"106":[73],"classes.":[74],"The":[75],"proposed":[76],"system":[77],"integrates":[78],"automatic":[79],"tablet-side":[80],"extraction,":[81],"heuristic":[82],"line":[83],"grouping,":[84],"n-gram-based":[86],"textual":[87,96],"similarity":[88],"evaluation":[89],"bridge":[91],"visual":[92],"structure,":[97],"achieves":[99],"consistent":[100],"improvements":[101],"up":[103],"28-37%":[105],"over":[106],"prior":[107],"work":[108],"on":[109],"COCO-style":[110],"metrics.":[112],"At":[113],"inference,":[114],"method":[116],"applied":[118],"87,668":[120],"tablet":[121,147],"fragments":[122],"from":[123],"Electronic":[125],"Babylonian":[126],"Library":[127],"(eBL)":[128],"corpus,":[129],"producing":[130],"nearly":[131],"2.9":[132],"detections.":[135],"Although":[136],"approach":[138],"operates":[139],"without":[140],"linguistic":[141,170],"priors":[142],"remains":[144],"sensitive":[145],"damage":[148],"layout":[150],"variability,":[151],"it":[152],"provides":[153],"scalable":[155],"interpretable":[157],"foundation":[158],"corpus-wide":[160],"analysis":[162],"supports":[164],"future":[165],"integration":[166],"with":[167],"multimodal":[168],"modelling":[171],"frameworks.":[172]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}
