Line Reconstruction and Segmentation of Words and Characters using Measures of Central Tendency and Measures of Dispersion

Aradhana Kar, S. Pradhan
{"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}
引用次数: 0

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用集中趋势和离散度量方法重建和分割文字
本研究的重点是在[1]中对论文的输出线段进行重建。在某些情况下,[1]的Line segmentation模块将一个行文本的字母和相关的矩阵分割成两个单独的线段。使用rebuild模块重建这些线段,以生成包含所有字母及其相关矩阵的行文本。该模块使用离散度的度量之一,即标准差来完成输出线段的重建。然后使用wordsegmentation Module从线段中分割单词。该模块使用集中趋势的一种度量,即均值和分散度的一种度量,即标准差来实现分词。然后使用字符分割模块从单词中分割字符。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Technological Empowerment: Applications of Machine Learning in Oral Healthcare Emotion Recognition From Online Classroom Videos Using Meta Learning Design and Development Recommendations for a Smart Weather Monitoring System Modified Convolutional Neural Network for Fashion Classification Challenges of Medical Text and Image Processing
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1