{"id":"https://openalex.org/W2148295954","doi":null,"title":"Word normalization for on-line handwritten word recognition","display_name":"Word normalization for on-line handwritten word recognition","publication_year":1994,"publication_date":"1994-01-01","ids":{"openalex":"https://openalex.org/W2148295954","mag":"2148295954"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.32.9273","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.9273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.research.att.com/~yann/exdb/publis/./psgz/bengio-lecun-94.ps.gz","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5086198262","display_name":"Yoshua Bengio","orcid":"https://orcid.org/0000-0002-9322-3515","observed_orcids":["https://orcid.org/0000-0002-9322-3515"]},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]},{"id":"https://openalex.org/I4387152956","display_name":"Alcatel-Lucent (France)","ror":"https://ror.org/024850w61","country_code":"FR","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I4210149358","https://openalex.org/I4387152956"]},{"id":"https://openalex.org/I45683168","display_name":"Polytechnique Montr\u00e9al","ror":"https://ror.org/05f8d4e86","country_code":"CA","type":"education","lineage":["https://openalex.org/I45683168"]}],"countries":["CA","FR","US"],"is_corresponding":false,"raw_author_name":"Yoshua Bengio","raw_affiliation_strings":["Alcatel-Lucent","AT&T","Ecole Polytechnique de Montreal"],"raw_orcid":"https://orcid.org/0000-0002-9322-3515","affiliations":[{"raw_affiliation_string":"Alcatel-Lucent","institution_ids":["https://openalex.org/I4387152956"]},{"raw_affiliation_string":"AT&T","institution_ids":["https://openalex.org/I1283103587"]},{"raw_affiliation_string":"Ecole Polytechnique de Montreal","institution_ids":["https://openalex.org/I45683168"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001226970","display_name":"Yann LeCun","orcid":"https://orcid.org/0000-0002-1992-2684","observed_orcids":["https://orcid.org/0000-0002-1992-2684"]},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yann LeCun","raw_affiliation_strings":["AT&T\u2021"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T\u2021","institution_ids":["https://openalex.org/I1283103587"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5719,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.71805168,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"409","last_page":"413"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.0,"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/word-normalization","display_name":"word normalization","score":0.8920000195503235},{"id":"https://openalex.org/keywords/handwritten-word-recognition","display_name":"handwritten word recognition","score":0.9319999814033508},{"id":"https://openalex.org/keywords/pen-computing","display_name":"pen-based computing","score":0.546999990940094},{"id":"https://openalex.org/keywords/cursive-writing","display_name":"cursive script","score":0.6200000047683716},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.6499999761581421},{"id":"https://openalex.org/keywords/hidden-markov-models","display_name":"hidden Markov models","score":0.8190000057220459},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.7929999828338623}],"concepts":[{"id":"https://openalex.org/C2778943297","wikidata":"https://www.wikidata.org/wiki/Q1079418","display_name":"Cursive","level":2,"score":0.8386607766151428},{"id":"https://openalex.org/C112640561","wikidata":"https://www.wikidata.org/wiki/Q2440634","display_name":"Handwriting recognition","level":3,"score":0.7790579795837402},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7589960694313049},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7536219358444214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6420584321022034},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6257206797599792},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.603145956993103},{"id":"https://openalex.org/C164086593","wikidata":"https://www.wikidata.org/wiki/Q1227035","display_name":"Stylus","level":2,"score":0.564616858959198},{"id":"https://openalex.org/C2779386606","wikidata":"https://www.wikidata.org/wiki/Q2393642","display_name":"Handwriting","level":2,"score":0.558987021446228},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5345361232757568},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49953341484069824},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4526505172252655},{"id":"https://openalex.org/C150856459","wikidata":"https://www.wikidata.org/wiki/Q8034367","display_name":"Word recognition","level":3,"score":0.42052289843559265},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34201890230178833},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.24168825149536133},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1772206425666809},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.32.9273","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.9273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.research.att.com/~yann/exdb/publis/./psgz/bengio-lecun-94.ps.gz","raw_type":"text"},{"id":"mag:2148295954","doi":null,"is_oa":false,"landing_page_url":"https://ieeexplore.ieee.org/abstract/document/576966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2049633694","https://openalex.org/W2055075080","https://openalex.org/W2111488376","https://openalex.org/W2148099973","https://openalex.org/W2162363099","https://openalex.org/W3207342693"],"related_works":["https://openalex.org/W2148695089","https://openalex.org/W2099070536","https://openalex.org/W7098963854","https://openalex.org/W6969081218","https://openalex.org/W2127940044","https://openalex.org/W2142503350","https://openalex.org/W2167544984","https://openalex.org/W7095506194","https://openalex.org/W7095816403","https://openalex.org/W2102836637"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2,59,61,73,90,115],"new":[3],"approach":[4],"to":[5,15,38,72,105],"normalizing":[6],"words":[7],"written":[8],"with":[9],"an":[10],"electronic":[11],"stylus":[12],"that":[13,76,117],"applies":[14],"all":[16],"styles":[17],"of":[18,31,52,92,109,152],"handwriting":[19,87],"(upper":[20],"case,":[21,23,99],"lower":[22,95],"printed,":[24,97],"cursive,":[25,100],"or":[26],"mixed).":[27],"A":[28],"geometrical":[29],"model":[30,57],"the":[32,39,43,50,53,56,107,150,153],"word":[33],"spatial":[34],"structure":[35],"is":[36,147],"fitted":[37],"pen":[40],"trajectory":[41,54],"using":[42],"EM":[44],"algorithm.":[45],"The":[46,66],"fitting":[47],"process":[48],"maximizes":[49],"likelihood":[51],"given":[55],"and":[58,70,80,101],"set":[60],"priors":[62],"on":[63],"its":[64,123],"parameters.":[65],"method":[67],"was":[68],"evaluated":[69],"integrated":[71],"recognition":[74],"system":[75],"combines":[77],"neural":[78],"networks":[79],"hidden":[81],"Markov":[82],"models.":[83],"1":[84],"Introduction":[85],"Natural":[86],"can":[88,141],"be":[89,142],"mixture":[91],"different":[93],"\"styles\",":[94],"case":[96],"upper":[98],"punctuation.":[102],"In":[103],"order":[104],"improve":[106],"success":[108],"penbased":[110],"computers,":[111],"we":[112],"would":[113],"like":[114],"recognizer":[116],"reliably":[118],"handles":[119],"such":[120],"handwriting,":[121],"but":[122],"implementation":[124],"faces":[125],"major":[126],"technical":[127],"challenges":[128],"[9].":[129],"It":[130],"has":[131],"been":[132],"long":[133],"known":[134],"that,":[135],"although":[136],"characters":[137],"taken":[138],"in":[139],"isolation":[140],"very":[143],"ambiguous,":[144],"considerable":[145],"information":[146],"available":[148],"from":[149],"context":[151],"whole":[154],"wo...":[155]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-09-21T07:43:02.447757","created_date":"2016-06-24T00:00:00"}