{"id":"https://openalex.org/W3134198399","doi":"https://doi.org/10.1109/icce-berlin50680.2020.9352194","title":"Deep Learning Methods for Indian Sign Language Recognition","display_name":"Deep Learning Methods for Indian Sign Language Recognition","publication_year":2020,"publication_date":"2020-11-09","ids":{"openalex":"https://openalex.org/W3134198399","doi":"https://doi.org/10.1109/icce-berlin50680.2020.9352194","mag":"3134198399"},"language":"en","primary_location":{"id":"doi:10.1109/icce-berlin50680.2020.9352194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-berlin50680.2020.9352194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin)","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/A5086687510","display_name":"Pratik Likhar","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pratik Likhar","raw_affiliation_strings":["Indian Institute of Science Bangalore,Department of Electrical Engineering,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science Bangalore,Department of Electrical Engineering,Bengaluru,India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021252259","display_name":"Neel Kamal Bhagat","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Neel Kamal Bhagat","raw_affiliation_strings":["Indian Institute of Science Bangalore,Department of Electrical Engineering,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science Bangalore,Department of Electrical Engineering,Bengaluru,India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042604366","display_name":"G. N. Rathna","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rathna G N","raw_affiliation_strings":["Indian Institute of Science Bangalore,Department of Electrical Engineering,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science Bangalore,Department of Electrical Engineering,Bengaluru,India","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":2.9778,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.9130157,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.996399998664856,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9947999715805054,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8280860185623169},{"id":"https://openalex.org/keywords/sign-language","display_name":"Sign language","score":0.7623744606971741},{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.709540069103241},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6821706295013428},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6693352460861206},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6601743102073669},{"id":"https://openalex.org/keywords/gesture-recognition","display_name":"Gesture recognition","score":0.5874307751655579},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5057814121246338},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.481880247592926},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.46438536047935486},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.44663095474243164},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41991230845451355},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34847667813301086}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8280860185623169},{"id":"https://openalex.org/C522192633","wikidata":"https://www.wikidata.org/wiki/Q34228","display_name":"Sign language","level":2,"score":0.7623744606971741},{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.709540069103241},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6821706295013428},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6693352460861206},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6601743102073669},{"id":"https://openalex.org/C159437735","wikidata":"https://www.wikidata.org/wiki/Q1519524","display_name":"Gesture recognition","level":3,"score":0.5874307751655579},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5057814121246338},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.481880247592926},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.46438536047935486},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.44663095474243164},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41991230845451355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34847667813301086},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-berlin50680.2020.9352194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce-berlin50680.2020.9352194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W22745672","https://openalex.org/W1901129140","https://openalex.org/W2005800054","https://openalex.org/W2016053056","https://openalex.org/W2151103935","https://openalex.org/W2471695703","https://openalex.org/W2609283227","https://openalex.org/W2738700117","https://openalex.org/W2769581371","https://openalex.org/W2782139629","https://openalex.org/W2803258434","https://openalex.org/W2885190481","https://openalex.org/W2890075545","https://openalex.org/W2913362834","https://openalex.org/W2962687137","https://openalex.org/W2963681914","https://openalex.org/W2998429707","https://openalex.org/W6639824700","https://openalex.org/W6682431704","https://openalex.org/W6737529583","https://openalex.org/W6751899635","https://openalex.org/W6754082841"],"related_works":["https://openalex.org/W4308478915","https://openalex.org/W4389049376","https://openalex.org/W1986488374","https://openalex.org/W3040456104","https://openalex.org/W2902873204","https://openalex.org/W2185750513","https://openalex.org/W2010878661","https://openalex.org/W3147379364","https://openalex.org/W2026258298","https://openalex.org/W3204639664"],"abstract_inverted_index":{"Indian":[0,119],"Sign":[1,120,149],"Language":[2,121,150],"is":[3,189,197],"the":[4,14,21,26,29,33,56,66,73,77,81,93,133,147,202,214,230,239,242,247,257,284],"language":[5,23],"used":[6,47,85,102,108,163,233],"by":[7,80],"specially":[8,30],"abled":[9,31],"people":[10],"in":[11,68,293],"India.":[12],"Unfortunately":[13],"general":[15,34],"population":[16],"has":[17],"no":[18],"understanding":[19],"of":[20,128,155,175,212,229,259,275,281],"sign":[22],"which":[24,254,270],"hampers":[25],"communication":[27],"between":[28],"and":[32,64,88,95,103,124,277],"population.":[35],"We":[36,45,220,232,265],"are":[37],"proposing":[38],"a":[39,61,125,140,152,159,172,209,250,261],"methodology":[40],"to":[41,50,91,112,200,216,222],"bridge":[42],"this":[43,52,183,194,224],"gap.":[44],"have":[46],"two":[48],"approaches":[49],"solve":[51,223],"problem.":[53],"First":[54],"using":[55,60,226,260],"depth+RGB":[57],"data":[58,78],"captured":[59],"Microsoft":[62,204],"Kinect":[63,205,263],"predicting":[65],"gestures":[67,117],"real":[69,294],"time.":[70,295],"For":[71],"segmenting":[72],"hand":[74,105,116],"region":[75],"from":[76,118,249],"obtained":[79],"RGB-D":[82,262],"camera":[83,253],"we":[84,144,181,185],"3D":[86],"reconstruction":[87],"affine":[89],"transformation":[90],"map":[92],"depth":[94],"RGB":[96,252],"information.":[97],"Convolutional":[98],"neural":[99],"networks":[100],"were":[101,107,122],"segmented":[104],"images/videos":[106],"as":[109,178,180,238],"an":[110,190,272,278],"input":[111,248],"them.":[113],"36":[114],"static":[115],"trained":[123],"classification":[126,153,173],"accuracy":[127,154,174],"98.81%":[129],"was":[130,162],"achieved":[131,171],"on":[132,283],"test":[134],"data.":[135,286],"This":[136,169],"model":[137,170],"also":[138],"showed":[139],"good":[141],"performance":[142],"when":[143],"transfer":[145],"learned":[146],"American":[148],"giving":[151],"97.71%.":[156],"LSTM":[157],"with":[158,193,208,218,235],"convolutional":[160],"kernel":[161],"for":[164,241],"training":[165,285],"10":[166],"dynamic":[167],"gestures.":[168],"99.08%.":[176],"But":[177],"soon":[179],"implemented":[182],"system,":[184],"figured":[186],"out":[187],"there":[188],"inherent":[191],"problem":[192,225],"methodology.":[195],"It":[196],"practically":[198],"unreasonable":[199],"carry":[201],"bulky":[203],"around":[206],"along":[207],"system":[210],"capable":[211],"performing":[213],"computation":[215],"communicate":[217],"people.":[219],"attempted":[221],"semantic":[227,268],"segmentation":[228,245,269],"hands.":[231],"U-Net":[234],"ResNet":[236],"101":[237],"backbone":[240],"same.":[243],"Semantic":[244],"utilises":[246],"normal":[251],"completely":[255],"removes":[256],"necessity":[258],"camera.":[264],"performed":[266,290],"multi-class":[267],"gave":[271],"IOU":[273],"score":[274,280],"0.9920":[276],"F1":[279],"0.9957":[282],"The":[287],"above":[288],"models":[289],"extremely":[291],"well":[292]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
