{"id":"https://openalex.org/W2115240329","doi":null,"title":"Integrated Segmentation and Recognition of Hand-Printed Numerals","display_name":"Integrated Segmentation and Recognition of Hand-Printed Numerals","publication_year":1990,"publication_date":"1990-10-01","ids":{"openalex":"https://openalex.org/W2115240329","mag":"2115240329"},"language":"en","primary_location":{"id":"mag:2115240329","doi":null,"is_oa":false,"landing_page_url":"http://papers.nips.cc/paper/397-integrated-segmentation-and-recognition-of-hand-printed-numerals.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null},"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/A5010632225","display_name":"James D. Keeler","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"James D. Keeler","raw_affiliation_strings":["MCC, Austin, TX#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MCC, Austin, TX#TAB#","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048240638","display_name":"David E. Rumelhart","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David E. Rumelhart","raw_affiliation_strings":["Psychology Department,Stanford University,Stanford,CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Psychology Department,Stanford University,Stanford,CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110150995","display_name":"Wee Kheng Leow","orcid":"https://orcid.org/0000-0001-9396-7664","observed_orcids":["https://orcid.org/0000-0001-9396-7664"]},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wee Kheng Leow","raw_affiliation_strings":["MCC and University of Texas, Austin, TX#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MCC and University of Texas, Austin, TX#TAB#","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7853,"has_fulltext":false,"cited_by_count":131,"citation_normalized_percentile":{"value":0.9310842,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"3","issue":null,"first_page":"557","last_page":"563"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9970999956130981,"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":0.9970999956130981,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.0010999999940395355,"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/T10320","display_name":"Neural Networks and Applications","score":0.0010999999940395355,"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/handwritten-digit-recognition","display_name":"handwritten digit recognition","score":0.75},{"id":"https://openalex.org/keywords/character-segmentation","display_name":"character segmentation","score":0.9240000247955322},{"id":"https://openalex.org/keywords/overlapping-characters","display_name":"overlapping characters","score":0.6269999742507935},{"id":"https://openalex.org/keywords/touching-characters","display_name":"touching characters","score":0.7049999833106995},{"id":"https://openalex.org/keywords/self-organizing-neural-networks","display_name":"self-organizing neural networks","score":0.7419999837875366},{"id":"https://openalex.org/keywords/competitive-learning","display_name":"competitive learning","score":0.7689999938011169},{"id":"https://openalex.org/keywords/backpropagation","display_name":"backpropagation","score":0.7289999723434448}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7738205194473267},{"id":"https://openalex.org/C204160518","wikidata":"https://www.wikidata.org/wiki/Q122653","display_name":"Numeral system","level":2,"score":0.7416180968284607},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7225615978240967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7224664092063904},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.6625517010688782},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6316231489181519},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6111533045768738},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.5638407468795776},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5298193693161011},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06865397095680237},{"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":"mag:2115240329","doi":null,"is_oa":false,"landing_page_url":"http://papers.nips.cc/paper/397-integrated-segmentation-and-recognition-of-hand-printed-numerals.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","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":4,"referenced_works":["https://openalex.org/W613766092","https://openalex.org/W2048060899","https://openalex.org/W2147800946","https://openalex.org/W2766736793"],"related_works":["https://openalex.org/W2129169401","https://openalex.org/W7095407058","https://openalex.org/W2105617997","https://openalex.org/W1963658102","https://openalex.org/W1596465034","https://openalex.org/W2115641419","https://openalex.org/W2155153031","https://openalex.org/W2067515543","https://openalex.org/W2035526030","https://openalex.org/W2068989008"],"abstract_inverted_index":{"Neural":[0],"network":[1,75],"algorithms":[2,30],"have":[3],"proven":[4],"useful":[5],"for":[6],"recognition":[7,14],"of":[8,22,112],"individual,":[9],"segmented":[10],"characters.":[11],"However,":[12],"their":[13],"accuracy":[15,21],"has":[16,88],"been":[17],"limited":[18],"by":[19],"the":[20,23,34,110,113,121],"underlying":[24],"segmentation":[25,29],"algorithm.":[26],"Conventional,":[27],"rule-based":[28],"encounter":[31],"difficulty":[32],"if":[33],"characters":[35],"are":[36],"touching,":[37],"broken,":[38],"or":[39],"noisy.":[40],"The":[41],"problem":[42],"in":[43,82,115],"these":[44],"situations":[45],"is":[46,57,68,100],"that":[47,77],"often":[48],"one":[49,60],"cannot":[50,61],"properly":[51,62],"segment":[52],"a":[53,64,73,94,116],"character":[54,65],"until":[55,66],"it":[56,67,92],"recognized":[58],"yet":[59],"recognize":[63],"segmented.":[69],"We":[70,124],"present":[71],"here":[72],"neural":[74],"algorithm":[76,87,97],"simultaneously":[78],"segments":[79],"and":[80,108],"recognizes":[81],"an":[83],"integrated":[84],"system.":[85],"This":[86],"several":[89],"novel":[90],"features:":[91],"uses":[93],"supervised":[95],"learning":[96],"(backpropagation),":[98],"but":[99],"able":[101],"to":[102,119],"take":[103],"position-independent":[104],"information":[105],"as":[106],"targets":[107],"self-organize":[109],"activities":[111],"units":[114],"competitive":[117],"fashion":[118],"infer":[120],"positional":[122],"information.":[123],"demonstrate":[125],"this":[126],"ability":[127],"with":[128],"overlapping":[129],"handprinted":[130],"numerals.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":10},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":5}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2016-06-24T00:00:00"}