{"id":"https://openalex.org/W7171567184","doi":"https://doi.org/10.48550/arxiv.2607.22723","title":"Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Models","display_name":"Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Models","publication_year":2026,"publication_date":"2026-07-22","ids":{"openalex":"https://openalex.org/W7171567184","doi":"https://doi.org/10.48550/arxiv.2607.22723"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.22723","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.22723","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.2607.22723","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061494645","display_name":"Huafu Li","orcid":"https://orcid.org/0000-0003-0680-5611"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Huafu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143787342","display_name":"Guo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Guo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143798865","display_name":"Jia Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Jia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143831789","display_name":"Lei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143793291","display_name":"Wei Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135084967","display_name":"Yun Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135080147","display_name":"Weijun Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Weijun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5143782251","display_name":"Liming Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Liming","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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.5884000062942505,"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":0.5884000062942505,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.26589998602867126,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.04490000009536743,"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/robustness","display_name":"Robustness (evolution)","score":0.6165000200271606},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.54339998960495},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.5364999771118164},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5024999976158142},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.42260000109672546},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.40400001406669617},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.383899986743927},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3752000033855438},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3686999976634979}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8084999918937683},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6165000200271606},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.551800012588501},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.54339998960495},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5364999771118164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5192000269889832},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5024999976158142},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48010000586509705},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.40400001406669617},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3752000033855438},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3686999976634979},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C9233905","wikidata":"https://www.wikidata.org/wiki/Q3276328","display_name":"Bidding","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2994000017642975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.2842999994754791},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.22723","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.22723","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.2607.22723","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.22723","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Visual":[0],"information":[1,106],"extraction":[2,64],"(VIE)":[3],"from":[4,62],"visually":[5],"rich":[6],"documents":[7],"remains":[8],"challenging":[9],"due":[10],"to":[11,73,156],"high":[12,52],"layout":[13],"variability":[14],"and":[15,33,65,104,141,159,168,190],"real-world":[16,114],"impairments.":[17],"Existing":[18],"methods":[19],"typically":[20],"rely":[21],"on":[22,112,125],"sequential":[23],"OCR":[24],"pipelines":[25],"or":[26],"end-to-end":[27],"models":[28,192],"requiring":[29],"extensive":[30],"labeled":[31],"data":[32],"layout-specific":[34],"training,":[35],"limiting":[36],"their":[37],"scalability.We":[38],"propose":[39],"a":[40,85,95,113,128],"classification-guided":[41],"large":[42],"vision-language":[43],"model":[44],"(LVLM)":[45],"framework":[46,172],"for":[47,178],"multi-type":[48],"VIE":[49],"that":[50,100],"achieves":[51],"accuracy":[53],"with":[54,117],"minimal":[55],"supervision.":[56],"The":[57,171],"approach":[58],"decouples":[59],"document-type":[60],"classification":[61],"content":[63],"employs":[66],"in-context":[67],"learning":[68],"(ICL)-based":[69],"dynamic":[70],"prompt":[71],"engineering":[72],"inject":[74],"task-specific":[75],"knowledge,":[76],"enabling":[77],"robust":[78],"zero-shot":[79,122],"inference":[80],"across":[81],"diverse":[82],"layouts.":[83],"From":[84],"theoretical":[86],"perspective,":[87],"the":[88],"proposed":[89],"method":[90,123],"can":[91],"be":[92],"viewed":[93],"as":[94],"form":[96],"of":[97],"conditional":[98],"computation":[99],"reduces":[101],"task":[102],"uncertainty":[103],"improves":[105,154],"efficiency":[107],"during":[108],"prompt-based":[109],"inference.":[110],"Evaluated":[111],"bidding":[115],"dataset":[116],"16":[118],"certificate":[119],"types,":[120],"our":[121],"(based":[124],"Qwen2.5-VL-7B)":[126],"outperforms":[127],"strong":[129],"supervised":[130],"baseline":[131],"by":[132],"18.35":[133],"percentage":[134],"points":[135],"in":[136,143,182],"F1-score":[137],"(86.43\\%":[138],"vs.":[139,148],"68.08\\%)":[140],"0.23":[142],"normalized":[144],"edit":[145],"distance":[146],"(0.90":[147],"0.67).":[149],"Optional":[150],"domain-specific":[151],"fine-tuning":[152],"further":[153],"performance":[155],"93.65\\%":[157],"F1":[158],"0.93":[160],"NED,":[161],"demonstrating":[162],"superior":[163],"robustness":[164],"against":[165],"seals,":[166],"watermarks,":[167],"low":[169],"contrast.":[170],"offers":[173],"an":[174],"efficient,":[175],"scalable":[176],"solution":[177],"complex":[179],"document":[180],"understanding":[181],"office":[183],"automation.":[184],"Code":[185],"is":[186],"available":[187],"at":[188,193],"https://github.com/FairmeHIT/Multi-VIE,":[189],"fine-tuned":[191],"https://huggingface.co/fairme/Qwen2.5-VL-7B-SFT.":[194]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-07-29T00:00:00"}
