{"title":"用集中趋势和离散度量方法重建和分割文字","authors":"Aradhana Kar, S. Pradhan","doi":"10.1109/ASSIC55218.2022.10088316","DOIUrl":null,"url":null,"abstract":"This research concentrates on reconstruction of the output line segments of the paper in [1]. The Line Segmenting module of [1] in some scenarios segments the alphabets and the associated matras of a line text in two separate line segments. These line segments are reconstructed using the Reconstruct Module to produce a line text with all its alphabets and its associated matras. This module uses one of the measures of dispersions, that is, standard deviation to accomplish reconstruction of output line segments. Then words are segmented from the line segments using WordSegmenting Module. This module uses one of the measures of central tendencies, i.e. mean and one of the measure of dispersions i.e, standard deviation to achieve word segmentation. Then characters are segmented from words using CharacterSegmenting Module.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Line Reconstruction and Segmentation of Words and Characters using Measures of Central Tendency and Measures of Dispersion\",\"authors\":\"Aradhana Kar, S. Pradhan\",\"doi\":\"10.1109/ASSIC55218.2022.10088316\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research concentrates on reconstruction of the output line segments of the paper in [1]. The Line Segmenting module of [1] in some scenarios segments the alphabets and the associated matras of a line text in two separate line segments. These line segments are reconstructed using the Reconstruct Module to produce a line text with all its alphabets and its associated matras. This module uses one of the measures of dispersions, that is, standard deviation to accomplish reconstruction of output line segments. Then words are segmented from the line segments using WordSegmenting Module. This module uses one of the measures of central tendencies, i.e. mean and one of the measure of dispersions i.e, standard deviation to achieve word segmentation. Then characters are segmented from words using CharacterSegmenting Module.\",\"PeriodicalId\":441406,\"journal\":{\"name\":\"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASSIC55218.2022.10088316\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASSIC55218.2022.10088316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Line Reconstruction and Segmentation of Words and Characters using Measures of Central Tendency and Measures of Dispersion
This research concentrates on reconstruction of the output line segments of the paper in [1]. The Line Segmenting module of [1] in some scenarios segments the alphabets and the associated matras of a line text in two separate line segments. These line segments are reconstructed using the Reconstruct Module to produce a line text with all its alphabets and its associated matras. This module uses one of the measures of dispersions, that is, standard deviation to accomplish reconstruction of output line segments. Then words are segmented from the line segments using WordSegmenting Module. This module uses one of the measures of central tendencies, i.e. mean and one of the measure of dispersions i.e, standard deviation to achieve word segmentation. Then characters are segmented from words using CharacterSegmenting Module.