IEEE International Conference on Document Analysis and Recognition最新文献_第8页

IEEE International Conference on Document Analysis and Recognition Pub Date : 2007-09-23 DOI: 10.1109/ICDAR.2007.127

B. Gatos, A. Antonacopoulos, N. Stamatopoulos

引用次数: 140

New Metrics for Evaluating Performance in Document Analysis Tasks_Application to the Table Case 在文档分析Tasks_Application to Table案例中评估性能的新指标

IEEE International Conference on Document Analysis and Recognition Pub Date : 2007-09-23 DOI: 10.1109/ICDAR.2007.176

A. C. E. Silva

引用次数: 12

Assessing and Improving the Quality of Document Images Acquired with Portable Digital Cameras 便携式数码相机获取的文件图像的质量评估与改进

IEEE International Conference on Document Analysis and Recognition Pub Date : 2007-09-23 DOI: 10.1109/ICDAR.2007.64

R. Lins, G. Silva, A. R. G. E. Silva

引用次数: 11

Dynamic TimeWarping Applied to Tamil Character Recognitio 动态时间翘曲在泰米尔字符识别中的应用

IEEE International Conference on Document Analysis and Recognition Pub Date : 2005-08-31 DOI: 10.1109/ICDAR.2005.96

R. Niels, L. Vuurpijl

引用次数: 15

From Humans to Handwriting to Computer and Back 从人类到手写再到电脑再回来

IEEE International Conference on Document Analysis and Recognition Pub Date : 2005-08-31 DOI: 10.1109/ICDAR.2005.116

C. Suen

{"title":"From Humans to Handwriting to Computer and Back","authors":"C. Suen","doi":"10.1109/ICDAR.2005.116","DOIUrl":"https://doi.org/10.1109/ICDAR.2005.116","url":null,"abstract":"Summary form only given. There has been much research on human thought processes, but little on the inference of such knowledge into the computer, for the purpose of handwriting recognition. Although modern recognition engines can recognize many handwritten symbols and cursive scripts with a high level of accuracy, they often make foolish or unreasonable mistakes. These engines often act like black boxes, which is why they make such mistakes on characters that would normally be easily recognized by human beings. To break through this level of accuracy, we have to look back and explore more human aspects, to better understand their thought processes and to discover the ways and means humans acquire recognition knowledge. After that, we can infer this knowledge to the computer to create more intelligent computational recognizers. This talk aims to share our findings with you related to the recognition of handwritten characters. It summarizes the results of several experiments we conducted in the past while attempting to understand the way humans write and recognize handwritten characters. Our investigations include handwriting education in elementary schools, handwriting models, stroke sequences, the legibility of different character shapes, left-handedness and right-handedness, the creation of databases for learning and testing, the derivation of the boundary between similar samples, and the pitfalls of current recognition algorithms and remedies. This talk concludes with highlights on the results of these studies and their applications to improve the reliability of computer recognition of handwritten characters.","PeriodicalId":294655,"journal":{"name":"IEEE International Conference on Document Analysis and Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127390700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Arabic Handwriting Recognition Competition 阿拉伯语笔迹识别比赛

IEEE International Conference on Document Analysis and Recognition Pub Date : 2005-08-31 DOI: 10.1109/ICDAR.2007.60

V. Märgner, H. E. Abed

引用次数: 192

Text Locating from Natural Scene Images Using Image Intensitie 利用图像强度从自然场景图像中定位文本

IEEE International Conference on Document Analysis and Recognition Pub Date : 2005-08-31 DOI: 10.1109/ICDAR.2005.232

Ji Soo Kim, Sang-Cheol Park, Soohyung Kim

引用次数: 25

Handwritten Numeral Recognition by means of Evolutionary Algorithms 基于进化算法的手写数字识别

IEEE International Conference on Document Analysis and Recognition Pub Date : 1999-09-20 DOI: 10.1109/ICDAR.1999.791910

C. Stefano, A. D. Cioppa, A. Marcelli

引用次数: 12

Introducing New Multiple Expert Decision Combination Topologies: A Case Study using Recognition of Handwritten Characters 引入新的多专家决策组合拓扑:以手写体字符识别为例

IEEE International Conference on Document Analysis and Recognition Pub Date : 1997-08-18 DOI: 10.1109/ICDAR.1997.620639

F. Rahman, M. Fairhurst

引用次数: 30

Problems and Approaches for Oriental Document Analysis 东方文献分析的问题与途径

IEEE International Conference on Document Analysis and Recognition Pub Date : 1997-08-18 DOI: 10.1109/ICDAR.1997.10004

J. H. Kim

{"title":"Problems and Approaches for Oriental Document Analysis","authors":"J. H. Kim","doi":"10.1109/ICDAR.1997.10004","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.10004","url":null,"abstract":"Machine understanding of hand,filled documents in China, Japan and Korea requires not only general solutions of document analysis but also ability to handle peculiarities of the Oriental languages. As expected, handwritten Chinese character recognition is the major task for it. In addition, Japanese Kana, Korean Hangul, Roman alphabet as well as numerals are targets of recognition. The main difficulties of Oriental character recognition originate from their large character sets. A practical system should be able to handle at least 5000 classes from possibly 50000 over classes. For Hangul, 11720 classes can be made in theory. The difficulty closely depends on writing styles. Oriental script is generally classified into regular, fluent, cursive style. Needless to say, cursive style is deformed most seriously and, therefore, most difficult to recognize. Regular style writing is often attacked successfully by feature matching and statistical analysis, while fluent style is now actively under investigation by stroke analyses and structural matching. Cursive style recognition is seldom found even in research papers. Since Chinese and Hangul characters are intrinsically hierarchical, often hierarchical analysis has been applied. A Hangul character, which corresponds a syllable, is formed by 2 to 5 basic graphemes, drawn from 24 classes, deploying them in two dimensional way. Recognizing component graphemes is, we believe, the viable approach to handle the large set of Hangul classes. Therefore, segmentation into graphemes, which is another difficult task, is the key for hierarchical recognition For robust recognition of fluent to cursive style, the following research directions are suggested.","PeriodicalId":294655,"journal":{"name":"IEEE International Conference on Document Analysis and Recognition","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128722916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0