{"id":"https://openalex.org/W7127883151","doi":"https://doi.org/10.48550/arxiv.2602.03913","title":"Entropy-Aware Structural Alignment for Zero-Shot Handwritten Chinese Character Recognition","display_name":"Entropy-Aware Structural Alignment for Zero-Shot Handwritten Chinese Character Recognition","publication_year":2026,"publication_date":"2026-02-03","ids":{"openalex":"https://openalex.org/W7127883151","doi":"https://doi.org/10.48550/arxiv.2602.03913"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.03913","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100901719","display_name":"Qiuming Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Luo, Qiuming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125155813","display_name":"Tao Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Tao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125215091","display_name":"Feng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Feng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017492492","display_name":"Heming Liu","orcid":"https://orcid.org/0009-0007-3400-5925"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Heming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125160652","display_name":"Rui Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mao, Rui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061703443","display_name":"Chang Kong","orcid":"https://orcid.org/0000-0002-1923-9877"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kong, Chang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100901719"],"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":0.9724000096321106,"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":0.9724000096321106,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.004800000227987766,"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":0.00430000014603138,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.669700026512146},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.667900025844574},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5284000039100647},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4018999934196472},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.40059998631477356},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3978999853134155},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.39579999446868896},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.37439998984336853},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3425999879837036}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.777999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6826000213623047},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.669700026512146},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.667900025844574},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5284000039100647},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.40059998631477356},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3978999853134155},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.3199999928474426},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2874999940395355},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.28060001134872437},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2793000042438507},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C2987247673","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Character recognition","level":3,"score":0.2727999985218048},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.26330000162124634},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2572999894618988},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.03913","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},{"id":"doi:10.48550/arxiv.2602.03913","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.03913","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.03913","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"sustainable_development_goals":[{"score":0.781199038028717,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Zero-shot":[0],"Handwritten":[1],"Chinese":[2],"Character":[3],"Recognition":[4],"(HCCR)":[5],"aims":[6],"to":[7,64,91,105,123],"recognize":[8],"unseen":[9],"characters":[10,21],"by":[11,128],"leveraging":[12],"radical-based":[13],"semantic":[14,133],"compositions.":[15],"However,":[16],"existing":[17,165],"approaches":[18],"often":[19],"treat":[20],"as":[22,73],"flat":[23],"radical":[24],"sequences,":[25],"neglecting":[26],"the":[27,31,51,125,130,158,169,174],"hierarchical":[28],"topology":[29],"and":[30,110],"uneven":[32],"information":[33],"density":[34],"of":[35,132,155],"different":[36],"components.":[37,83],"To":[38],"address":[39],"these":[40],"limitations,":[41],"we":[42,58,85],"propose":[43],"an":[44,60,100,153],"Entropy-Aware":[45],"Structural":[46],"Alignment":[47],"Network":[48],"that":[49,77,145],"bridges":[50],"visual-semantic":[52],"gap":[53],"through":[54,139],"information-theoretic":[55],"modeling.":[56],"First,":[57],"introduce":[59],"Information":[61],"Entropy":[62],"Prior":[63],"dynamically":[65],"modulate":[66],"positional":[67],"embeddings":[68],"via":[69,99],"multiplicative":[70],"interaction,":[71],"acting":[72],"a":[74,87,115],"saliency":[75],"detector":[76],"prioritizes":[78],"discriminative":[79],"roots":[80],"over":[81],"ubiquitous":[82],"Second,":[84],"construct":[86],"Dual-View":[88],"Radical":[89],"Tree":[90],"extract":[92],"multi-granularity":[93],"structural":[94],"features,":[95],"which":[96],"are":[97],"integrated":[98],"adaptive":[101],"Sigmoid-based":[102],"gating":[103],"network":[104],"encode":[106],"both":[107],"global":[108],"layout":[109],"local":[111],"spatial":[112],"roles.":[113],"Finally,":[114],"Top-K":[116],"Semantic":[117],"Feature":[118],"Fusion":[119],"mechanism":[120],"is":[121],"devised":[122],"augment":[124],"decoding":[126],"process":[127],"utilizing":[129],"centroid":[131],"neighbors,":[134],"effectively":[135],"rectifying":[136],"visual":[137],"ambiguities":[138],"feature-level":[140],"consensus.":[141],"Extensive":[142],"experiments":[143],"demonstrate":[144],"our":[146],"method":[147],"establishes":[148],"new":[149],"state-of-the-art":[150],"performance,":[151],"achieving":[152,187],"accuracy":[154,189],"55.04\\%":[156],"on":[157],"ICDAR":[159],"2013":[160],"dataset":[161],"($m=1500$),":[162],"significantly":[163],"outperforming":[164],"CLIP-based":[166],"baselines":[167],"in":[168],"challenging":[170],"zero-shot":[171],"setting.":[172],"Furthermore,":[173],"framework":[175],"exhibits":[176],"exceptional":[177],"data":[178],"efficiency,":[179],"demonstrating":[180],"rapid":[181],"adaptability":[182],"with":[183,190],"minimal":[184],"support":[185,193],"samples,":[186],"92.41\\%":[188],"only":[191],"one":[192],"sample":[194],"per":[195],"class.":[196]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-07T00:00:00"}
