{"id":"https://openalex.org/W2030781528","doi":"https://doi.org/10.1142/s0218001493000340","title":"OFF LINE RECOGNITION OF HANDWRITTEN POSTAL WORDS USING NEURAL NETWORKS","display_name":"OFF LINE RECOGNITION OF HANDWRITTEN POSTAL WORDS USING NEURAL NETWORKS","publication_year":1993,"publication_date":"1993-08-01","ids":{"openalex":"https://openalex.org/W2030781528","doi":"https://doi.org/10.1142/s0218001493000340","mag":"2030781528"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001493000340","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001493000340","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-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/A5069708056","display_name":"Chris Burges","orcid":null},"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":"C.J.C. BURGES","raw_affiliation_strings":["AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111617162","display_name":"J. Ben","orcid":null},"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":"J.I. BEN","raw_affiliation_strings":["AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108674311","display_name":"J. S. Denker","orcid":null},"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":"J.S. DENKER","raw_affiliation_strings":["AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001226970","display_name":"Yann LeCun","orcid":null},"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":"Y. LECUN","raw_affiliation_strings":["AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA","institution_ids":["https://openalex.org/I1283103587"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006281427","display_name":"C.R. Nohl","orcid":null},"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":"C.R. NOHL","raw_affiliation_strings":["AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Bell Laboratories, Room 2G-237 Crawfords Corner Road, Holmdel, NJ 07733, USA","institution_ids":["https://openalex.org/I1283103587"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5647,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.70642324,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"07","issue":"04","first_page":"689","last_page":"704"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9907000064849854,"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":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7906564474105835},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6892992258071899},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6487558484077454},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6186974048614502},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5880851745605469},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4834233522415161},{"id":"https://openalex.org/keywords/handwriting-recognition","display_name":"Handwriting recognition","score":0.48315852880477905},{"id":"https://openalex.org/keywords/neocognitron","display_name":"Neocognitron","score":0.4513286352157593},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.43660399317741394},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4149984121322632},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4143398702144623},{"id":"https://openalex.org/keywords/time-delay-neural-network","display_name":"Time delay neural network","score":0.35463932156562805},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.27738696336746216},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10012698173522949}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7906564474105835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6892992258071899},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6487558484077454},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6186974048614502},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5880851745605469},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4834233522415161},{"id":"https://openalex.org/C112640561","wikidata":"https://www.wikidata.org/wiki/Q2440634","display_name":"Handwriting recognition","level":3,"score":0.48315852880477905},{"id":"https://openalex.org/C121144440","wikidata":"https://www.wikidata.org/wiki/Q669754","display_name":"Neocognitron","level":4,"score":0.4513286352157593},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.43660399317741394},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4149984121322632},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4143398702144623},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.35463932156562805},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27738696336746216},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10012698173522949},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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:10.1142/s0218001493000340","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001493000340","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2106606153","https://openalex.org/W4282920028","https://openalex.org/W2261468771","https://openalex.org/W2166713742","https://openalex.org/W3215620389","https://openalex.org/W3092663764","https://openalex.org/W2371791833","https://openalex.org/W2069265260","https://openalex.org/W2104698839","https://openalex.org/W2389191077"],"abstract_inverted_index":{"We":[0,23,137,197],"describe":[1,24,222],"a":[2,13,81,105,113,134,141,171],"method,":[3],"\u201cShortest":[4],"Path":[5],"Segmentation\u201d":[6],"(SPS),":[7],"which":[8,112,125],"combines":[9],"dynamic":[10,60],"programming":[11,61],"and":[12,19,38,67,70,94,123,165],"neural":[14,75],"net":[15],"recognizer":[16],"for":[17,80,159],"segmenting":[18],"recognizing":[20],"character":[21],"strings.":[22],"the":[25,44,50,55,59,65,74,119,168,181,185,190,202,214],"application":[26],"of":[27,34,40,84,144,155,161,184,193,224,226],"this":[28,176],"method":[29,51],"to":[30,52,73,131,204],"two":[31],"problems:":[32],"recognition":[33,39],"handwritten":[35,41,87,98],"ZIP":[36,45,91],"Codes,":[37,46],"words.":[42],"For":[43,97],"we":[47,101],"also":[48,199,221],"used":[49,130],"automatically":[53],"segment":[54],"images":[56,154,194],"during":[57,195],"training:":[58],"stage":[62],"both":[63],"performs":[64],"segmentation":[66,192],"provides":[68],"inputs":[69],"desired":[71],"outputs":[72],"network.":[76],"Results":[77],"are":[78,128],"reported":[79],"test":[82,142],"set":[83,143],"2642":[85],"unsegmented":[86],"212":[88],"dpi":[89],"binary":[90],"Code":[92],"(5-":[93],"9-digit)":[95],"images.":[96],"word":[99,121,150],"recognition,":[100],"combined":[102],"SPS":[103,126],"with":[104],"\u201cSpace":[106],"Displacement":[107],"Neural":[108],"Network\u201d":[109],"approach,":[110],"in":[111,124,175],"single-character-recognition":[114],"network":[115],"is":[116],"extended":[117],"over":[118],"entire":[120],"image,":[122],"techniques":[127],"then":[129],"rank":[132],"order":[133],"given":[135],"lexicon.":[136],"report":[138],"results":[139],"on":[140],"3000":[145],"300":[146],"ppi":[147],"gray":[148],"scale":[149],"images,":[151],"extracted":[152],"from":[153],"live":[156],"mail":[157],"pieces,":[158],"lexicons":[160],"size":[162],"10,":[163],"100,":[164],"1000.":[166],"Representing":[167],"problem":[169],"as":[170,173],"graph":[172],"proposed":[174],"paper":[177,219],"has":[178],"advantages":[179],"beyond":[180],"efficient":[182],"finding":[183,213],"final":[186],"optimal":[187],"segmentation,":[188],"or":[189],"automatic":[191],"training.":[196],"can":[198],"easily":[200],"extend":[201],"technique":[203],"generate":[205],"K":[206,215],"\u201crunner":[207],"up\u201d":[208],"answers":[209],"(for":[210],"example,":[211],"by":[212],"shortest":[216],"paths).":[217],"This":[218],"will":[220],"applications":[223],"some":[225],"these":[227],"ideas.":[228]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}