检索结果-南通市图书馆

Roman to Gurmukhi Social Media text normalization

International Journal of Intelligent Computing and Cybernetics 2020年第4期13卷 407-435页

作者： Jagroop Kaur Jaswinder Singh Department of Computer Science and Engineering Punjabi UniversityPatialaIndia

Purpose-normalization is an important step in all the natural language processing applications that are handling social media *** text from social media poses a different kind of problems that are not present in regular ***,a considerable amount of work has been done in this direction,but mostly in the English *** who do not speak English code mixed the text with their native language and posted text on social media using the Roman *** kind of text further aggravates the problem of *** paper aims to discuss the concept of normalization with respect to code-mixed social media text,and a model has been proposed to normalize such ***/methodology/approach-The system is divided into two phases-candidate generation and most probable sentence *** generation task is treated as machine translation task where the Roman text is treated as source language and Gurmukhi text is treated as the target *** translation system has been proposed to generate candidate *** candidates are generated,the second phase uses the beam search method for selecting the most probable sentence based on hidden Markov ***-Character error rate(CER)and bilingual evaluation understudy(BLEU)score are *** proposed system has been compared with Akhar software and RB\_R2G system,which are also capable of transliterating Roman text to *** performance of the system outperforms Akhar *** CER and BLEU scores are 0.268121 and 0.6807939,respectively,for ill-formed *** limitations/implications-It was observed that the system produces dialectical variations of a word or the word with minor errors like diacritic *** checker can improve the output of the system by correcting these minor *** experimentation is needed for optimizing language identifier,which will further help in improving the *** language model also seeks further ***

关键词： Social media text text normalization Code-mixed text Deep neural network Language model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Enhanced Attention-Based Encoder-Decoder Framework for text Recognition

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2071-2086页

作者： S.Prabu K.Joseph Abraham Sundar School of Computing SASTRA Deemed to be UniversityThanjavur613401India

Recognizing irregular text in natural images is a challenging task in computer *** existing approaches still face difﬁculties in recognizing irre-gular text because of its diverse *** this paper,we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder *** proposed framework is divided into four main ***,in the image transformation module,a Thin Plate Spline(TPS)transformation is employed to transform the irregular text image into a readable text ***-ondly,we propose a novel Spatial Attention Module(SAM)to compel the model to concentrate on text regions and obtain enriched feature ***,a deep bi-directional long short-term memory(Bi-LSTM)network is used to make a con-textual feature map out of a visual feature map generated from a Convolutional Neural Network(CNN).Finally,we propose a Dual Step Attention Mechanism(DSAM)integrated with the Connectionist Temporal Classiﬁcation(CTC)-Attention decoder to re-weights visual features and focus on the intra-sequence relationships to generate a more accurate character *** effectiveness of our proposed framework is veriﬁed through extensive experiments on various benchmarks datasets,such as SVT,ICDAR,CUTE80,and *** perfor-mance of the proposed text recognition framework is analyzed with the accuracy *** that our proposed method outperforms the existing approaches on both regular and irregular ***,the robustness of our approach is evaluated using the grocery datasets,such as GroZi-120,Web-Market,SKU-110K,and Freiburg Groceries datasets that contain complex text ***,our framework produces superior performance on grocery datasets.

关键词： Deep learning text recognition text normalization attention mechanism convolutional neural network(CNN)

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：