{"id":"https://openalex.org/W4297541820","doi":"https://doi.org/10.3390/make4040043","title":"Automatic Extraction of Medication Information from Cylindrically Distorted Pill Bottle Labels","display_name":"Automatic Extraction of Medication Information from Cylindrically Distorted Pill Bottle Labels","publication_year":2022,"publication_date":"2022-09-27","ids":{"openalex":"https://openalex.org/W4297541820","doi":"https://doi.org/10.3390/make4040043"},"language":"en","primary_location":{"id":"doi:10.3390/make4040043","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make4040043","pdf_url":"https://www.mdpi.com/2504-4990/4/4/43/pdf?version=1664271134","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/4/4/43/pdf?version=1664271134","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077539423","display_name":"Kseniia Gromova","orcid":"https://orcid.org/0000-0003-4948-2103"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kseniia Gromova","raw_affiliation_strings":["Computer Science Program, Division of Science and Engineering, Penn State Abington, Abington, PA 19001, USA"],"raw_orcid":"https://orcid.org/0000-0003-4948-2103","affiliations":[{"raw_affiliation_string":"Computer Science Program, Division of Science and Engineering, Penn State Abington, Abington, PA 19001, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067669769","display_name":"Vinayak Elangovan","orcid":"https://orcid.org/0000-0002-1386-6394"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vinayak Elangovan","raw_affiliation_strings":["Computer Science Program, Division of Science and Engineering, Penn State Abington, Abington, PA 19001, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Program, Division of Science and Engineering, Penn State Abington, Abington, PA 19001, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5067669769"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1559},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1559},"fwci":0.8125,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.70814365,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"4","issue":"4","first_page":"852","last_page":"864"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9855999946594238,"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":0.9855999946594238,"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/T14189","display_name":"Intravenous Infusion Technology and Safety","score":0.9584000110626221,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13970","display_name":"Pharmacy and Medical Practices","score":0.9487000107765198,"subfield":{"id":"https://openalex.org/subfields/3004","display_name":"Pharmacology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6480890512466431},{"id":"https://openalex.org/keywords/pill","display_name":"Pill","score":0.6313900947570801},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5867540836334229},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.5557719469070435},{"id":"https://openalex.org/keywords/medical-prescription","display_name":"Medical prescription","score":0.4988889694213867},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4813973903656006},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.452427476644516},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3961215615272522},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3870853781700134},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3460933566093445},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.3283979892730713},{"id":"https://openalex.org/keywords/nursing","display_name":"Nursing","score":0.16252145171165466},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.11039447784423828}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6480890512466431},{"id":"https://openalex.org/C81603835","wikidata":"https://www.wikidata.org/wiki/Q3457430","display_name":"Pill","level":2,"score":0.6313900947570801},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5867540836334229},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.5557719469070435},{"id":"https://openalex.org/C2426938","wikidata":"https://www.wikidata.org/wiki/Q3355478","display_name":"Medical prescription","level":2,"score":0.4988889694213867},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4813973903656006},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.452427476644516},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3961215615272522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3870853781700134},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3460933566093445},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.3283979892730713},{"id":"https://openalex.org/C159110408","wikidata":"https://www.wikidata.org/wiki/Q121176","display_name":"Nursing","level":1,"score":0.16252145171165466},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.11039447784423828},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/make4040043","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make4040043","pdf_url":"https://www.mdpi.com/2504-4990/4/4/43/pdf?version=1664271134","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ff03d8cfd72947c3a70f46e434d9ba25","is_oa":true,"landing_page_url":"https://doaj.org/article/ff03d8cfd72947c3a70f46e434d9ba25","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 4, Iss 4, Pp 852-864 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-4990/4/4/43/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/make4040043","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction; Volume 4; Issue 4; Pages: 852-864","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/make4040043","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make4040043","pdf_url":"https://www.mdpi.com/2504-4990/4/4/43/pdf?version=1664271134","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4297541820.pdf"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1515683276","https://openalex.org/W1633844846","https://openalex.org/W1901129140","https://openalex.org/W2083420124","https://openalex.org/W2095905764","https://openalex.org/W2101234009","https://openalex.org/W2126060993","https://openalex.org/W2135785083","https://openalex.org/W2145023731","https://openalex.org/W2307770531","https://openalex.org/W2558850922","https://openalex.org/W2574070988","https://openalex.org/W2580929962","https://openalex.org/W2771278513","https://openalex.org/W2784089855","https://openalex.org/W2899418290","https://openalex.org/W2916798096","https://openalex.org/W2946948417","https://openalex.org/W2947596730","https://openalex.org/W2947903144","https://openalex.org/W2954996726","https://openalex.org/W2963163009","https://openalex.org/W2963402313","https://openalex.org/W2963474899","https://openalex.org/W2964304707","https://openalex.org/W3015803803","https://openalex.org/W3018020238","https://openalex.org/W3113834142","https://openalex.org/W4385245566","https://openalex.org/W6675354045","https://openalex.org/W6681053624","https://openalex.org/W6684973550","https://openalex.org/W6739901393","https://openalex.org/W6748056645","https://openalex.org/W7073767605"],"related_works":["https://openalex.org/W2281596493","https://openalex.org/W2361820086","https://openalex.org/W2385522019","https://openalex.org/W2992184181","https://openalex.org/W4242814848","https://openalex.org/W4230097952","https://openalex.org/W2414997714","https://openalex.org/W2769812682","https://openalex.org/W1843227692","https://openalex.org/W2274307249"],"abstract_inverted_index":{"Patient":[0],"compliance":[1,223],"with":[2],"prescribed":[3],"medication":[4,41,56,77,90,228],"regimens":[5],"is":[6,62],"critical":[7],"for":[8,97],"maintaining":[9],"health":[10,221],"and":[11,14,28,58,64,110,137,174,193,222],"managing":[12],"disease":[13],"illness.":[15],"To":[16],"encourage":[17],"patient":[18,51,187,220],"compliance,":[19],"multiple":[20,167],"aids,":[21,47],"like":[22],"automatic":[23],"pill":[24,26],"dispensers,":[25],"organizers,":[27],"various":[29,138],"reminder":[30],"applications,":[31],"have":[32,74],"been":[33],"developed":[34],"to":[35,39,67,83,88,160,218],"help":[36],"people":[37],"adhere":[38],"their":[40,55],"regimens.":[42],"However,":[43],"when":[44],"utilizing":[45],"these":[46],"the":[48,80,162,166,200],"user":[49],"or":[50],"must":[52],"manually":[53],"enter":[54,89],"information":[57,78,91,100],"schedule.":[59,229],"This":[60,93,114,140],"process":[61],"time-consuming":[63],"often":[65],"prone":[66],"error.":[68],"For":[69],"example,":[70],"elderly":[71],"patients":[72],"may":[73],"difficulty":[75],"reading":[76],"on":[79],"bottle":[81],"due":[82],"decreased":[84],"eyesight,":[85],"leading":[86],"them":[87],"incorrectly.":[92],"study":[94,115,214],"explored":[95],"methods":[96],"extracting":[98],"pertinent":[99],"from":[101,199],"cylindrically":[102],"distorted":[103],"prescription":[104],"drug":[105,189,191],"labels":[106],"using":[107,203],"Machine":[108],"Learning":[109],"Computer":[111],"Vision":[112],"techniques.":[113,208],"found":[116],"that":[117],"Deep":[118],"Convolutional":[119],"Neural":[120],"Networks":[121],"(DCNN)":[122],"performed":[123],"better":[124],"than":[125],"other":[126],"techniques":[127],"in":[128,212],"identifying":[129],"label":[130,169],"key":[131,155],"points":[132,148,156],"under":[133],"different":[134],"lighting":[135],"conditions":[136],"backgrounds.":[139],"method":[141],"achieved":[142],"a":[143],"percentage":[144],"of":[145,152,195],"Correct":[146],"Key":[147],"PCK":[149],"@":[150],"0.03":[151],"97%.":[153],"These":[154],"were":[157,171,197],"then":[158],"used":[159,217],"correct":[161],"cylindrical":[163],"distortion.":[164],"Next,":[165],"dewarped":[168],"images":[170],"stitched":[172],"together":[173],"processed":[175],"by":[176,224],"an":[177,226],"Optical":[178],"Character":[179],"Recognition":[180],"(OCR)":[181],"engine.":[182],"Pertinent":[183],"information,":[184],"such":[185],"as":[186],"name,":[188,190],"strength,":[192],"directions":[194],"use,":[196],"extracted":[198],"recognized":[201],"text":[202],"Natural":[204],"Language":[205],"Processing":[206],"(NLP)":[207],"The":[209],"system":[210],"created":[211],"this":[213],"can":[215],"be":[216],"improve":[219],"creating":[225],"accurate":[227]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2022-09-29T00:00:00"}
