具有手写文字识别功能的智能扫描仪

S. Meher, D. Basa
{"title":"具有手写文字识别功能的智能扫描仪","authors":"S. Meher, D. Basa","doi":"10.1109/ICSENST.2011.6137038","DOIUrl":null,"url":null,"abstract":"Character recognition plays an important role in the modern world. It can solve more complex problems and make human's job easier. Difficulties in recognition of handwritten text in Indian scripts include extreme cursiveness in their handwritten form due to the presence of vowel modifiers and compound characters. Here we propose a simple yet robust structural solution for recognizing handwritten characters in Odia (the official language of Odisha, a state in Republic of India). In the proposed system, a given text is segmented into lines and then each line is segmented into individual words and then each word is segmented into individual characters or basic symbols. Basic symbols are identified as the fundamental units of segmentation used for recognition. All the characters are divided into two groups (Group-I and Group-II) according to the property i.e. whether a vertical line is present or absent at the right-most part. All the characters of the two groups are resized into 20×14 pixels, which are directly subjected to train the two neural networks (one for Group-I and another for Group-II). Using the proposed system we have found better result for proper recognition rate as compared to other methods. The proposed sensing system is also found to be efficient in compressing the script data quite efficiently.","PeriodicalId":202062,"journal":{"name":"2011 Fifth International Conference on Sensing Technology","volume":"153 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"An intelligent scanner with handwritten odia character recognition capability\",\"authors\":\"S. Meher, D. Basa\",\"doi\":\"10.1109/ICSENST.2011.6137038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Character recognition plays an important role in the modern world. It can solve more complex problems and make human's job easier. Difficulties in recognition of handwritten text in Indian scripts include extreme cursiveness in their handwritten form due to the presence of vowel modifiers and compound characters. Here we propose a simple yet robust structural solution for recognizing handwritten characters in Odia (the official language of Odisha, a state in Republic of India). In the proposed system, a given text is segmented into lines and then each line is segmented into individual words and then each word is segmented into individual characters or basic symbols. Basic symbols are identified as the fundamental units of segmentation used for recognition. All the characters are divided into two groups (Group-I and Group-II) according to the property i.e. whether a vertical line is present or absent at the right-most part. All the characters of the two groups are resized into 20×14 pixels, which are directly subjected to train the two neural networks (one for Group-I and another for Group-II). Using the proposed system we have found better result for proper recognition rate as compared to other methods. The proposed sensing system is also found to be efficient in compressing the script data quite efficiently.\",\"PeriodicalId\":202062,\"journal\":{\"name\":\"2011 Fifth International Conference on Sensing Technology\",\"volume\":\"153 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Fifth International Conference on Sensing Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSENST.2011.6137038\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Fifth International Conference on Sensing Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSENST.2011.6137038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

字符识别在现代社会中扮演着重要的角色。它可以解决更复杂的问题,使人类的工作更容易。识别印度手写体文本的困难包括由于元音修饰语和复合字的存在,手写体形式极其草莽。在这里,我们提出了一个简单而健壮的结构解决方案,用于识别Odia(印度共和国Odisha邦的官方语言)中的手写字符。在该系统中,将给定文本分割成行,然后将每行分割成单个单词,然后将每个单词分割成单个字符或基本符号。基本符号被确定为用于识别的分割的基本单位。所有汉字根据其属性(即最右侧是否有竖线)分为两组(组i和组ii)。将两组的所有字符调整为20×14像素,直接训练两个神经网络(一个用于组i,另一个用于组ii)。与其他方法相比,本文提出的系统具有更好的识别率。所提出的传感系统在压缩脚本数据方面也非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An intelligent scanner with handwritten odia character recognition capability
Character recognition plays an important role in the modern world. It can solve more complex problems and make human's job easier. Difficulties in recognition of handwritten text in Indian scripts include extreme cursiveness in their handwritten form due to the presence of vowel modifiers and compound characters. Here we propose a simple yet robust structural solution for recognizing handwritten characters in Odia (the official language of Odisha, a state in Republic of India). In the proposed system, a given text is segmented into lines and then each line is segmented into individual words and then each word is segmented into individual characters or basic symbols. Basic symbols are identified as the fundamental units of segmentation used for recognition. All the characters are divided into two groups (Group-I and Group-II) according to the property i.e. whether a vertical line is present or absent at the right-most part. All the characters of the two groups are resized into 20×14 pixels, which are directly subjected to train the two neural networks (one for Group-I and another for Group-II). Using the proposed system we have found better result for proper recognition rate as compared to other methods. The proposed sensing system is also found to be efficient in compressing the script data quite efficiently.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信