{"id":"https://openalex.org/W2151312373","doi":"https://doi.org/10.1109/icdar.2015.7333801","title":"Learning non-Markovian constraints for handwriting recognition","display_name":"Learning non-Markovian constraints for handwriting recognition","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W2151312373","doi":"https://doi.org/10.1109/icdar.2015.7333801","mag":"2151312373"},"language":"en","primary_location":{"id":"doi:10.1109/icdar.2015.7333801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdar.2015.7333801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 13th International Conference on Document Analysis and Recognition (ICDAR)","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/A5044441607","display_name":"Ryosuke Kakisako","orcid":null},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryosuke Kakisako","raw_affiliation_strings":["Kyushu University, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051387162","display_name":"Seiichi Uchida","orcid":"https://orcid.org/0000-0001-8592-7566"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Seiichi Uchida","raw_affiliation_strings":["Kyushu University, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084881849","display_name":"Volkmar Frinken","orcid":null},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Frinken Volkmar","raw_affiliation_strings":["Kyushu University, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5044441607"],"corresponding_institution_ids":["https://openalex.org/I135598925"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09316698,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"446","last_page":"450"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9950000047683716,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9950000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.791627049446106},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.646493136882782},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6238250136375427},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.6096501350402832},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5451743602752686},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4473091661930084},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39055734872817993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3678721785545349},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3296201229095459},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3265060782432556},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24422481656074524}],"concepts":[{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.791627049446106},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.646493136882782},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6238250136375427},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.6096501350402832},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5451743602752686},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4473091661930084},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39055734872817993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3678721785545349},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3296201229095459},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3265060782432556},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24422481656074524},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdar.2015.7333801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdar.2015.7333801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 13th International Conference on Document Analysis and Recognition (ICDAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1546972969","https://openalex.org/W1549521126","https://openalex.org/W1797715124","https://openalex.org/W1999659031","https://openalex.org/W2014915963","https://openalex.org/W2020319818","https://openalex.org/W2151133732","https://openalex.org/W2153906892","https://openalex.org/W2161236525","https://openalex.org/W2168311572","https://openalex.org/W2952793010","https://openalex.org/W6632941271","https://openalex.org/W6638292638"],"related_works":["https://openalex.org/W2341338763","https://openalex.org/W2950183183","https://openalex.org/W2419138286","https://openalex.org/W2507549656","https://openalex.org/W2050318184","https://openalex.org/W101156158","https://openalex.org/W2182848507","https://openalex.org/W2387825067","https://openalex.org/W2506414444","https://openalex.org/W1797715124"],"abstract_inverted_index":{"Recently,":[0],"the":[1,25,35,43,70,78,85,99,111,120],"horizon":[2],"of":[3,42,62],"dynamic":[4],"time":[5,53],"warping":[6],"(DTW)":[7],"for":[8,73],"matching":[9,26,36,100],"two":[10],"sequential":[11],"patterns":[12],"has":[13,115],"been":[14],"extended":[15],"to":[16,48,66],"deal":[17],"with":[18],"non-Markovian":[19,22,44,79,95],"constraints.":[20],"The":[21,39,59,81],"constraints":[23,33],"regulate":[24,34],"in":[27,51,128],"a":[28,55,90,93,116],"wider":[29],"scale,":[30],"whereas":[31],"Markovian":[32,91,113,122],"only":[37],"locally.":[38],"global":[40],"optimization":[41],"DTW":[45,114,123],"is":[46,65,69,88],"proved":[47,109],"be":[49,125],"solvable":[50],"polynomial":[52],"by":[54,76],"graph":[56],"cut":[57],"algorithm.":[58],"main":[60],"contribution":[61],"this":[63],"paper":[64],"reveal":[67],"what":[68],"best":[71,86],"constraint":[72,87,96],"handwriting":[74],"recognition":[75],"using":[77],"DTW.":[80],"result":[82],"showed":[83],"that":[84,97,105,110],"not":[89],"but":[92],"totally":[94],"regulates":[98],"between":[101],"very":[102],"distant":[103],"points;":[104],"is,":[106],"it":[107],"was":[108],"conventional":[112],"clear":[117],"limitation":[118],"and":[119],"non-":[121],"should":[124],"more":[126],"focused":[127],"future":[129],"research.":[130]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
