{"id":"https://openalex.org/W2970732541","doi":"https://doi.org/10.1109/icip.2019.8803226","title":"A Holistic Recognition Approach for Woodblock-Print Mongolian Words Based on Convolutional Neural Network","display_name":"A Holistic Recognition Approach for Woodblock-Print Mongolian Words Based on Convolutional Neural Network","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970732541","doi":"https://doi.org/10.1109/icip.2019.8803226","mag":"2970732541"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803226","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5050639942","display_name":"Hongxi Wei","orcid":"https://orcid.org/0000-0002-2570-4544"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongxi Wei","raw_affiliation_strings":["Inner Mongolia University, Hohhot, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University, Hohhot, China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076174513","display_name":"Guanglai Gao","orcid":"https://orcid.org/0009-0005-5513-1192"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanglai Gao","raw_affiliation_strings":["Inner Mongolia University, Hohhot, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University, Hohhot, China","institution_ids":["https://openalex.org/I2722730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2722730"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2726","last_page":"2730"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9991000294685364,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9897000193595886,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8125049471855164},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7932583093643188},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7852123975753784},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.7440445423126221},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6253325939178467},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6032153367996216},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5867735147476196},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.497041255235672},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4941709339618683},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37108901143074036},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09811457991600037}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8125049471855164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7932583093643188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7852123975753784},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.7440445423126221},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6253325939178467},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6032153367996216},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5867735147476196},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.497041255235672},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4941709339618683},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37108901143074036},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09811457991600037},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","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},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8803226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803226","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W242877468","https://openalex.org/W329217948","https://openalex.org/W1904365287","https://openalex.org/W1976752808","https://openalex.org/W1978426462","https://openalex.org/W1978964824","https://openalex.org/W1998327639","https://openalex.org/W2033154814","https://openalex.org/W2054832683","https://openalex.org/W2056225014","https://openalex.org/W2087334050","https://openalex.org/W2097117768","https://openalex.org/W2100721436","https://openalex.org/W2112796928","https://openalex.org/W2141125852","https://openalex.org/W2148143831","https://openalex.org/W2161381512","https://openalex.org/W2163605009","https://openalex.org/W2170866695","https://openalex.org/W2293167034","https://openalex.org/W2342840038","https://openalex.org/W2399247562","https://openalex.org/W2573612273","https://openalex.org/W2752634058","https://openalex.org/W2902270960","https://openalex.org/W2962779710","https://openalex.org/W6640036494","https://openalex.org/W6672424381","https://openalex.org/W6684191040","https://openalex.org/W6697147753","https://openalex.org/W6712513890"],"related_works":["https://openalex.org/W2591697403","https://openalex.org/W2953716828","https://openalex.org/W2904857019","https://openalex.org/W2944728705","https://openalex.org/W3011538607","https://openalex.org/W2904022177","https://openalex.org/W2359348847","https://openalex.org/W4321441197","https://openalex.org/W4294432981","https://openalex.org/W1522196789"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposed":[2,126],"a":[3,12],"holistic":[4],"recognition":[5],"approach":[6,127],"for":[7],"woodblock-print":[8,64],"Mongolian":[9,65],"words":[10],"using":[11],"convolutional":[13],"neural":[14],"network":[15],"(CNN).":[16],"To":[17],"be":[18,36,72,81,111],"specific,":[19],"the":[20,32,39,60,63,77,84,89,107,115,125,129],"whole":[21],"word":[22,33,66],"image":[23],"is":[24,99,118],"regarded":[25],"as":[26],"input":[27],"of":[28,91],"CNN.":[29,46],"Hence,":[30],"all":[31],"images":[34,67],"should":[35],"normalized":[37],"into":[38,45,74],"same":[40],"size":[41,52],"before":[42],"being":[43],"inputted":[44],"By":[47],"comparison,":[48],"an":[49],"appropriate":[50],"normalization":[51],"has":[53],"been":[54],"determined":[55],"in":[56],"our":[57,95],"study.":[58],"Through":[59],"above":[61],"manner,":[62],"do":[68],"not":[69],"need":[70],"to":[71,87,101],"segmented":[73],"glyphs.":[75],"Thereby,":[76],"segmentation":[78,130],"errors":[79],"can":[80,110],"avoided":[82],"under":[83],"circumstance.":[85],"Furthermore,":[86],"solve":[88],"problem":[90],"imbalance":[92],"distribution":[93],"on":[94],"dataset,":[96],"SMOTE":[97],"technique":[98],"adopted":[100],"generate":[102],"samples.":[103],"In":[104],"this":[105],"way,":[106],"training":[108],"procedure":[109],"more":[112,119],"efficient":[113],"and":[114,133],"obtained":[116],"CNN":[117],"robust.":[120],"Experimental":[121],"results":[122],"demonstrate":[123],"that":[124],"outperforms":[128],"based":[131],"method":[132],"other":[134],"baselines.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
