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2009 10th International Conference on Document Analysis and Recognition最新文献

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Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm 基于约束AdaBoost算法的复杂场景图像文本检测与定位
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.172
S. Hanif, L. Prevost
{"title":"Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm","authors":"S. Hanif, L. Prevost","doi":"10.1109/ICDAR.2009.172","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.172","url":null,"abstract":"We have proposed a complete system for text detection and localization in gray scale scene images. A boosting framework integrating feature and weak classifier selection based on computational complexity is proposed to construct efficient text detectors. The proposed scheme uses a small set of heterogeneous features which are spatially combined to build a large set of features. A neural network based localizer learns necessary rules for localization. The evaluation is done on the challenging ICDAR 2003 robust reading and text locating database. The results are encouraging and our system can localize text of various font sizes and styles in complex background.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127921116","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: 125
A Unified Framework Based on the Level Set Approach for Segmentation of Unconstrained Double-Sided Document Images Suffering from Bleed-Through 基于水平集方法的无约束双面文档图像分割的统一框架
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.108
R. F. Moghaddam, David Rivest-Hénault, Itay Bar Yosef, M. Cheriet
{"title":"A Unified Framework Based on the Level Set Approach for Segmentation of Unconstrained Double-Sided Document Images Suffering from Bleed-Through","authors":"R. F. Moghaddam, David Rivest-Hénault, Itay Bar Yosef, M. Cheriet","doi":"10.1109/ICDAR.2009.108","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.108","url":null,"abstract":"A novel method for the segmentation of double-sided ancient document images suffering from bleed-through effect is presented. It takes advantage of the level set framework to provide a completely integrated process for the segmentation of the text along with the removal of the bleed-through interfering patterns. This process is driven by three forces: 1) a binarization force based on an adaptive global threshold is used to identify region of low intensity, 2) a reverse diffusion force allows for the separation of interfering patterns from the true text, and 3) a small regularization force favors smooth boundaries. This integrated method achieves high quality results at reasonable computational cost, and can easily host other concepts to enhance its performance. The method is successfully applied to real and synthesized degraded document images. Also, the registration problem of the double-sided document images is addressed by introducing a level set method; the results are promising.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130026488","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: 11
Recognition of Degraded Handwritten Characters Using Local Features 利用局部特征识别退化手写字符
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.158
Markus Diem, Robert Sablatnig
{"title":"Recognition of Degraded Handwritten Characters Using Local Features","authors":"Markus Diem, Robert Sablatnig","doi":"10.1109/ICDAR.2009.158","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.158","url":null,"abstract":"The main problems of Optical Character Recognition (OCR) systems are solved if printed latin text is considered. Since OCR systems are based upon binary images, their results are poor if the text is degraded. In this paper a codex consisting of ancient manuscripts is investigated. Due to environmental effects the characters of the analyzed codex are washed out which leads to poor results gained by state of the art binarization methods. Hence, a segmentation free approach based on local descriptors is being developed. Regarding local information allows for recognizing characters that are only partially visible. In order to recognize a character the local descriptors are initially classified with a Support Vector Machine (SVM) and then identified by a voting scheme of neighboring local descriptors. State of the art local descriptor systems are evaluated in this paper in order to compare their performance for the recognition of degraded characters.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130085791","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: 41
Slit Style HOG Feature for Document Image Word Spotting 狭缝风格HOG特征的文件图像单词识别
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.118
Kengo Terasawa, Yuzuru Tanaka
{"title":"Slit Style HOG Feature for Document Image Word Spotting","authors":"Kengo Terasawa, Yuzuru Tanaka","doi":"10.1109/ICDAR.2009.118","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.118","url":null,"abstract":"This paper presents a word spotting method based on line-segmentation, sliding window, continuous dynamic programming, and slit style HOG feature. Our method is applicable regardless of what language is written in the manuscript because it does not require any language-dependent preprocess. The slit style HOG feature is a gradient-distribution-based feature with overlapping normalization and redundant expression, and the use of this feature improved the performance of the word spotting. We compared our method with some previously developed word spotting methods, and confirmed that our method outperforms them in both English and Japanese manuscripts.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129381359","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: 101
Bayesian Best-First Search for Pattern Recognition - Application to Address Recognition 模式识别中的贝叶斯最佳优先搜索-在地址识别中的应用
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.231
Tomoyuki Hamamura, T. Akagi, Bunpei Irie
{"title":"Bayesian Best-First Search for Pattern Recognition - Application to Address Recognition","authors":"Tomoyuki Hamamura, T. Akagi, Bunpei Irie","doi":"10.1109/ICDAR.2009.231","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.231","url":null,"abstract":"In this paper, we propose a novel algorithm “Bayesian Best-First Search (BB Search)”, for use in search problems in pattern recognition, such as address recognition.BB search uses “a posteriori” probability for the evaluation value in best-first search. BB search is more flexible to changing time limits compared to beam search used in conventional pattern recognition approach. It does not need designing a heuristic function for each problem like A* search.We demonstrated a 12.4% improvement over beam search on an address recognition experiment using real postal images.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130944012","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
A Dual Taxonomy for Defects in Digitized Historical Photos 数字化历史照片缺陷的双重分类
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.134
E. Ardizzone, A. D. Polo, H. Dindo, G. Mazzola, C. Nanni
{"title":"A Dual Taxonomy for Defects in Digitized Historical Photos","authors":"E. Ardizzone, A. D. Polo, H. Dindo, G. Mazzola, C. Nanni","doi":"10.1109/ICDAR.2009.134","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.134","url":null,"abstract":"Old photos may be affected by several types of defects. Manual restorers use their own taxonomy to classify damages by which a photo is affected, in order to apply the proper restoration techniques for a specific defect. Once a photo is digitally acquired, defects become part of the image, and their aspect change. This paper wants to be a first attempt to correlate real defects of printed photos, and digital defects of their digitized versions. A dual taxonomy is proposed, for real and digital defects, and used to classify an image dataset, for a posteriori comparative study. Furthermore, a set of digital features is analyzed for digitized images, to identify which of them could be useful for an automatic inspection method.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132930878","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: 6
Error-Correcting Output Coding for the Convolutional Neural Network for Optical Character Recognition 用于光学字符识别的卷积神经网络纠错输出编码
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.144
Huiqun Deng, G. Stathopoulos, C. Suen
{"title":"Error-Correcting Output Coding for the Convolutional Neural Network for Optical Character Recognition","authors":"Huiqun Deng, G. Stathopoulos, C. Suen","doi":"10.1109/ICDAR.2009.144","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.144","url":null,"abstract":"It is known that convolutional neural networks (CNNs) are efficient for optical character recognition (OCR) and many other visual classification tasks. This paper applies error-correcting output coding (ECOC) to the CNN for segmentation-free OCR such that: 1) the CNN target outputs are designed according to code words of length N; 2) the minimum Hamming distance of the code words is designed to be as large as possible given N. ECOC provides the CNN with the ability to reject or correct output errors to reduce character insertions and substitutions in the recognized text. Also, using code words instead of letter images as the CNN target outputs makes it possible to construct an OCR for a new language without designing the letter images as the target outputs. Experiments on the recognition of English letters, 10 digits, and some special characters show the effectiveness of ECOC in reducing insertions and substitutions.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133168626","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: 21
Offline Signature Verification Based on Pseudo-Cepstral Coefficients 基于伪倒谱系数的离线签名验证
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.68
J. Vargas-Bonilla, M. A. Ferrer-Ballester, C. Travieso-González, J. B. Alonso
{"title":"Offline Signature Verification Based on Pseudo-Cepstral Coefficients","authors":"J. Vargas-Bonilla, M. A. Ferrer-Ballester, C. Travieso-González, J. B. Alonso","doi":"10.1109/ICDAR.2009.68","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.68","url":null,"abstract":"Features representing information about pressure distribution from a static image of a handwritten signature are analyzed for an offline verification system. From gray-scale images, its histogram is calculated and used as \"spectrum'' for calculation of pseudo-cepstral coefficients. Finally, the unique minimum-phase sequence is estimated and used as feature vector for signature verification. The optimal number of pseudo-coefficients is estimated for best system performance. Experiments were carried out using a database containing signatures from 100 individuals. The robustness of the analyzed system for simple forgeries is tested out with a LS-SVM model. For the sake of completeness, a comparison of the results obtained by the proposed approach with similar works published using pseudo-dynamic feature for offline signature verification is presented.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131620918","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: 2
High Performance Chinese/English Mixed OCR with Character Level Language Identification 具有字符级语言识别的高性能中英文混合OCR
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.14
Kai Wang, Jianming Jin, Qingren Wang
{"title":"High Performance Chinese/English Mixed OCR with Character Level Language Identification","authors":"Kai Wang, Jianming Jin, Qingren Wang","doi":"10.1109/ICDAR.2009.14","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.14","url":null,"abstract":"Currently, there have been several high performance OCR products for Chinese or for English. However, no one OCR technique can be simultaneously fit for both the English and the Chinese due to the large differences between Chinese and English. On the other hand, Chinese/English mixed document increases drastically with the globalization, so it is rather important to study the Chinese/English mixed document processing. Obviously, the key problem to resolve is how to split the mixed document into two parts: Chinese part and English part, so that the different OCR techniques can be applied to different parts. To further improve the previous system performance, a novel Chinese/English split algorithm based on global information is proposed and a rule for language identification is achieved by Bayesian formula. Experiment shows, the system error rate drops from 1.52% to 0.87% on magazine samples and from 1.32% to 0.75% on book samples, more than 2/5 of errors are excluded, which provides an experimental support for our research work.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133796339","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: 9
Comparative Study of Devnagari Handwritten Character Recognition Using Different Feature and Classifiers 基于不同特征和分类器的Devnagari手写体字符识别比较研究
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.244
U. Pal, T. Wakabayashi, F. Kimura
{"title":"Comparative Study of Devnagari Handwritten Character Recognition Using Different Feature and Classifiers","authors":"U. Pal, T. Wakabayashi, F. Kimura","doi":"10.1109/ICDAR.2009.244","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.244","url":null,"abstract":"In recent years research towards Indian handwritten character recognition is getting increasing attention. Many approaches have been proposed by the researchers towards handwritten Indian character recognition and many recognition systems for isolated handwritten numerals/characters are available in the literature. To get idea of the recognition results of different classifiers and to provide new benchmark for future research, in this paper a comparative study of Devnagari handwritten character recognition using twelve different classifiers and four sets of feature is presented. Projection distance, subspace method, linear discriminant function, support vector machines, modified quadratic discriminant function, mirror image learning, Euclidean distance, nearest neighbour, k-Nearest neighbour, modified projection distance, compound projection distance, and compound modified quadratic discriminant function are used as different classifiers. Feature sets used in the classifiers are computed based on curvature and gradient information obtained from binary as well as gray-scale images.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115574815","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: 134
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