{"id":"https://openalex.org/W2030276403","doi":"https://doi.org/10.1109/icdar.2007.4377048","title":"Handwritten Chinese Character Recognition Using Modified LDA and Kernel FDA","display_name":"Handwritten Chinese Character Recognition Using Modified LDA and Kernel FDA","publication_year":2007,"publication_date":"2007-09-01","ids":{"openalex":"https://openalex.org/W2030276403","doi":"https://doi.org/10.1109/icdar.2007.4377048","mag":"2030276403"},"language":"en","primary_location":{"id":"doi:10.1109/icdar.2007.4377048","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdar.2007.4377048","pdf_url":null,"source":{"id":"https://openalex.org/S4210215987","display_name":"Proceedings of the International Conference on Document Analysis and Recognition","issn_l":"1520-5363","issn":["1520-5363","2379-2140"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101562721","display_name":"Degui Yang","orcid":"https://orcid.org/0000-0003-1604-9792"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"D. Yang","raw_affiliation_strings":["School of Electronics and Information, South China University of Technology, Guangzhou, China","South China University of Technology,guangzhou"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"South China University of Technology,guangzhou","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107956252","display_name":"Ling Jin","orcid":"https://orcid.org/0009-0005-3005-7870"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"L. Jin","raw_affiliation_strings":["School of Electronics and Information, South China University of Technology, Guangzhou, China","South China University of Technology,guangzhou"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"South China University of Technology,guangzhou","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101562721"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.8782,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65450399,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"12","issue":null,"first_page":"914","last_page":"918"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9951000213623047,"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/T10057","display_name":"Face and Expression Recognition","score":0.9951000213623047,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9944999814033508,"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":0.9922000169754028,"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/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.7445029020309448},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6682519912719727},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.658244788646698},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6225503087043762},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5796158313751221},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.5026090145111084},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4998817443847656},{"id":"https://openalex.org/keywords/chinese-characters","display_name":"Chinese characters","score":0.44905829429626465},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.44638630747795105},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.43082237243652344},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20126166939735413},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.11197632551193237},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.06955355405807495}],"concepts":[{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.7445029020309448},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682519912719727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.658244788646698},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6225503087043762},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5796158313751221},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.5026090145111084},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4998817443847656},{"id":"https://openalex.org/C2781051154","wikidata":"https://www.wikidata.org/wiki/Q8201","display_name":"Chinese characters","level":2,"score":0.44905829429626465},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.44638630747795105},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.43082237243652344},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20126166939735413},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.11197632551193237},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.06955355405807495},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdar.2007.4377048","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdar.2007.4377048","pdf_url":null,"source":{"id":"https://openalex.org/S4210215987","display_name":"Proceedings of the International Conference on Document Analysis and Recognition","issn_l":"1520-5363","issn":["1520-5363","2379-2140"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321026","display_name":"Ministry of Earth Sciences","ror":"https://ror.org/013cf5k59"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"},{"id":"https://openalex.org/F4320324544","display_name":"Korea Food and Drug Administration","ror":"https://ror.org/01f7dp456"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1756033901","https://openalex.org/W2001003881","https://openalex.org/W2040193698","https://openalex.org/W2041657594","https://openalex.org/W2096925994","https://openalex.org/W2134262590","https://openalex.org/W2140095548","https://openalex.org/W2150796457","https://openalex.org/W2156909104","https://openalex.org/W6638098620","https://openalex.org/W6674732917"],"related_works":["https://openalex.org/W2141981133","https://openalex.org/W2386228546","https://openalex.org/W2086055175","https://openalex.org/W2365801610","https://openalex.org/W2388048830","https://openalex.org/W2150796457","https://openalex.org/W2016459271","https://openalex.org/W2566759662","https://openalex.org/W2788610776","https://openalex.org/W3145966574"],"abstract_inverted_index":{"The":[0],"effectiveness":[1,120],"of":[2,22,98,104,112,121],"kernel":[3],"fisher":[4],"discrimination":[5],"analysis":[6,72],"(KFDA)":[7],"has":[8],"been":[9],"demonstrated":[10],"by":[11],"many":[12],"pattern":[13,36],"recognition":[14,37,43,79,92,113],"applications.":[15],"However,":[16],"due":[17],"to":[18,25,29,32,76,88],"the":[19,64,78,82,90,119,122],"large":[20,34],"size":[21],"Gram":[23],"matrix":[24],"be":[26],"trained,":[27],"how":[28],"use":[30],"KFDA":[31,54,85],"solve":[33],"vocabulary":[35],"task":[38],"such":[39],"as":[40],"Chinese":[41,60,99],"Characters":[42],"is":[44,56,74,86,115],"still":[45],"a":[46,52,67,109],"challenging":[47],"problem.":[48],"In":[49,63,81],"this":[50],"paper,":[51],"two-stage":[53],"approach":[55],"presented":[57],"for":[58],"handwritten":[59],"character":[61,100],"recognition.":[62],"first":[65],"stage,":[66,84],"new":[68],"modified":[69],"linear":[70],"discriminant":[71],"method":[73],"developed":[75],"get":[77],"candidates.":[80],"second":[83],"used":[87],"determine":[89],"final":[91],"result.":[93],"Experiments":[94],"on":[95],"1034":[96],"categories":[97],"from":[101],"120":[102],"sets":[103],"handwriting":[105],"samples":[106],"shows":[107],"that":[108],"3.37%":[110],"improvement":[111],"rate":[114],"obtained,":[116],"which":[117],"suggests":[118],"proposed":[123],"method.":[124]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
