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

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Vector Representation of Graphs: Application to the Classification of Symbols and Letters 图的向量表示:应用于符号和字母的分类
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.218
Nicolas Sidère, P. Héroux, Jean-Yves Ramel
{"title":"Vector Representation of Graphs: Application to the Classification of Symbols and Letters","authors":"Nicolas Sidère, P. Héroux, Jean-Yves Ramel","doi":"10.1109/ICDAR.2009.218","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.218","url":null,"abstract":"In this article we present a new approach for the classification of structured data using graphs. We suggest to solve the problem of complexity in measuring the distance between graphs by using a new graph signature. We present an extension of the vector representation based on pattern frequency, which integrates labeling information. In this paper, we compare the results achieved on public graph databases for the classification of symbols and letters using this graph signature with those obtained using the graph edit distance.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"6 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":"126155190","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: 26
Application of Multi-Level Classifiers and Clustering for Automatic Word Spotting in Historical Document Images 多层次分类器和聚类在历史文献图像自动词识别中的应用
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.104
R. F. Moghaddam, M. Cheriet
{"title":"Application of Multi-Level Classifiers and Clustering for Automatic Word Spotting in Historical Document Images","authors":"R. F. Moghaddam, M. Cheriet","doi":"10.1109/ICDAR.2009.104","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.104","url":null,"abstract":"A complete system for preprocessing and word spotting of very old historical document images is presented. Document images are processed for extraction of salient information using a word spotting technique which does not need line and word segmentation and is language independent.A multi-class library of connected components of document text is created based on six features. The spotting is performed using Euclidean distance measure enhanced by rotation and dynamic time wrapping transforms. The method is applied to a dataset from Juma Al Majid Center (Dubai)with promising results. A promising performance of the word spotting technique is obtained using an automatic preprocessing stage. In this stage, using content-level classifiers, accurate stroke pixels are extracted in a robust way. The preprocessed document images are also more legible to the end user and are less costly to archive and transfer.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"4 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":"126169305","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: 60
Biometric Person Authentication Method Using Camera-Based Online Signature Acquisition 基于摄像头的在线签名采集生物识别身份验证方法
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.112
D. Muramatsu, Kumiko Yasuda, T. Matsumoto
{"title":"Biometric Person Authentication Method Using Camera-Based Online Signature Acquisition","authors":"D. Muramatsu, Kumiko Yasuda, T. Matsumoto","doi":"10.1109/ICDAR.2009.112","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.112","url":null,"abstract":"A camera-based online signature verification system is proposed in this paper. One web camera is used for data acquisition, and a sequential Monte Carlo method is used for tracking a pen tip. Several distances are computed from an online signature, and a fusion model trained by using AdaBoost combines the distances and computes a final score.Preliminary experiments were performed by using a private database. The proposed system yielded an equal error rate (EER) of 4.0%.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"22 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120993626","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: 13
“The Godfather” vs. “Chaos”: Comparing Linguistic Analysis Based on On-line Knowledge Sources and Bags-of-N-Grams for Movie Review Valence Estimation 《教父》与《混沌》:基于在线知识来源的语言分析与基于n- grams的电影评论评价的比较
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.194
Björn Schuller, J. Schenk, G. Rigoll, T. Knaup
{"title":"“The Godfather” vs. “Chaos”: Comparing Linguistic Analysis Based on On-line Knowledge Sources and Bags-of-N-Grams for Movie Review Valence Estimation","authors":"Björn Schuller, J. Schenk, G. Rigoll, T. Knaup","doi":"10.1109/ICDAR.2009.194","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.194","url":null,"abstract":"In the fields of sentiment and emotion recognition, bag of words modeling has lately become popular for the estimation of valence in text. A typical application is the evaluation of reviews of e. g. movies, music, or games. In this respect we suggest the use of back-off N-Grams as basis for a vector space construction in order to combine advantages of word-order modeling and easy integration into potential acoustic feature vectors intended for spoken document retrieval. For a fine granular estimate we consider data-driven regression next to classification based on Support Vector Machines. Alternatively the on-line knowledge sources ConceptNet, General Inquirer, and WordNet not only serve to reduce out-of-vocabulary events, but also as basis for a purely linguistic analysis. As special benefit, this approach does not demand labeled training data. A large set of 100 k movie reviews of 20 years stemming from Metacritic is utilized throughout extensive parameter discussion and comparative evaluation effectively demonstrating efficiency of the proposed methods.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"30 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":"121022775","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: 24
Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation 基于识别的手写体数字字符串分割的统计建模与学习
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.25
Yanjie Wang, Xiabi Liu, Yunde Jia
{"title":"Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation","authors":"Yanjie Wang, Xiabi Liu, Yunde Jia","doi":"10.1109/ICDAR.2009.25","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.25","url":null,"abstract":"This paper proposes a recognition based approach to handwritten numeral string segmentation. We consider two classes: numeral strings segmented correctly or not. The feature vectors containing recognition information for numeral strings segmented correctly are assumed to be of the distribution of Gaussian mixture model (GMM). Based on this modeling, the recognition based segmentation is solved under the Max-Min posterior Pseudo-probabilities (MMP) framework of learning Bayesian classifiers. In the training phase, we use the MMP method to learn a posterior pseudo-probability measure function from positive samples and negative samples of numeral strings segmented correctly. In the process of recognition based segmentation, we generate all possible candidate segmentations of an input string through contour and profile analysis, and then compute the posterior pseudo-probabilities of being the numeral string segmented correctly for all the candidate segmentations. The candidate segmentation with the maximum posterior pseudo-probability is taken as the final result. The effectiveness of our approach is demonstrated by the experiments of numeral string segmentation and recognition on the NIST SD19 database.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"43 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":"128027886","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: 5
A Unified Framework for Recognizing Handwritten Chemical Expressions 手写化学表达式识别的统一框架
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.64
Ming Chang, Shi Han, Dongmei Zhang
{"title":"A Unified Framework for Recognizing Handwritten Chemical Expressions","authors":"Ming Chang, Shi Han, Dongmei Zhang","doi":"10.1109/ICDAR.2009.64","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.64","url":null,"abstract":"Chemical expressions have more variant structures in 2-D space than that in math equations. In this paper we propose a unified framework for recognizing handwritten chemical expressions including both inorganic and organic expressions. A set of novel statistical algorithms is presented in two key components of this framework: symbol grouping and structure analysis. Non-symbol modeling and inter-group modeling are proposed to achieve better grouping result, and bond modeling is proposed to group the special bond symbols in the unified framework. A graph-based representation (CESG) is defined for representing generic chemical expressions, and the structure analysis problem is formulated as a search problem for CESG over a weighted direction graph. Experiments on a database of more than 35,000 expressions were conducted and results are presented.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"37 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":"128174649","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: 10
Writer Adaptive Training and Writing Variant Model Refinement for Offline Arabic Handwriting Recognition 离线阿拉伯手写识别的写作者自适应训练和书写变体模型改进
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.9
P. Dreuw, David Rybach, C. Gollan, H. Ney
{"title":"Writer Adaptive Training and Writing Variant Model Refinement for Offline Arabic Handwriting Recognition","authors":"P. Dreuw, David Rybach, C. Gollan, H. Ney","doi":"10.1109/ICDAR.2009.9","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.9","url":null,"abstract":"We present a writer adaptive training and writer clustering approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their variations. Additionally, a writing variant model refinement for specific writing variants is proposed.Current approaches try to compensate the impact of different writing styles during preprocessing and normalization steps.Writer adaptive training with a CMLLR based feature adaptation is used to train writer dependent models. An unsupervised writer clustering with Bayesian information criterion based stopping condition for a CMLLR based feature adaptation during a two-pass decoding process is used to cluster different handwriting styles of unknown test writers.The proposed methods are evaluated on the IFN/ENIT Arabic handwriting database.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"54 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":"124816967","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: 42
GMs in On-Line Handwritten Whiteboard Note Recognition: The Influence of Implementation and Modeling 在线手写白板笔记识别中的gm:实现和建模的影响
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.127
J. Schenk, Benedikt Hörnler, Björn Schuller, Artur Braun, G. Rigoll
{"title":"GMs in On-Line Handwritten Whiteboard Note Recognition: The Influence of Implementation and Modeling","authors":"J. Schenk, Benedikt Hörnler, Björn Schuller, Artur Braun, G. Rigoll","doi":"10.1109/ICDAR.2009.127","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.127","url":null,"abstract":"We present a comparison of two state-of-the-art toolboxes for implementing Graphical Models (GMs), namely the HTK and the GMTK, and their use for discrete on-line handwritten whiteboard note recognition. We then motivate a GM that is capable of modeling the statistical dependencies between the pen’s pressure information and the remaining features after vector quantization. Since the number of variable parameters rises when more codebook entries are used for quantization, the proposed model outperforms standard HMMs for low numbers of codebook entries.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"14 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":"124104439","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
Trinary Image Mosaicing Based Watermark String Detection 基于三元图像拼接的水印字符串检测
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.82
Jun Sun, S. Naoi, Y. Fujii, Hiroaki Takebe, Y. Hotta
{"title":"Trinary Image Mosaicing Based Watermark String Detection","authors":"Jun Sun, S. Naoi, Y. Fujii, Hiroaki Takebe, Y. Hotta","doi":"10.1109/ICDAR.2009.82","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.82","url":null,"abstract":"Watermark string detection is very useful for document security protection. In [2], we proposed an image based document watermark detection system. In this paper, two major modifications are made based on the previous system.First, a trinary image mosaicing algorithm is proposed to merge the broken watermark string images into a high quality mosaic image. Second, we relax the conditions in the Maximum Clique based keyword detection algorithm.The new algorithm can handle watermark string with variant spacing. Therefore, not only English keywords, but also Japanese/Chinese keywords can be processed. The Experimental results show the effectiveness of our algorithm.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"223 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":"124238939","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
A Method for Automatically Extracting Road Layers from Raster Maps 一种栅格地图道路层自动提取方法
2009 10th International Conference on Document Analysis and Recognition Pub Date : 2009-07-26 DOI: 10.1109/ICDAR.2009.274
Yao-Yi Chiang, Craig A. Knoblock
{"title":"A Method for Automatically Extracting Road Layers from Raster Maps","authors":"Yao-Yi Chiang, Craig A. Knoblock","doi":"10.1109/ICDAR.2009.274","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.274","url":null,"abstract":"To exploit the road network in raster maps, the first step is to extract the pixels that constitute the roads and then vectorize the road pixels. Identifying colors that represent roads in raster maps for extracting road pixels is difficult since raster maps often contain numerous colors due to the noise introduced during the processes of image compression and scanning. In this paper, we present an approach that minimizes the required user input for identifying the road colors representing the road network in a raster map. We can then use the identified road colors to extract road pixels from the map. Our approach can be used on scanned and compressed maps that are otherwise difficult to process automatically and tedious to process manually. We tested our approach with 100 maps from a variety of sources, which include 90 scanned maps with various compression levels and 10 computer generated maps. We successfully identified the road colors and extracted the road pixels from all test maps with fewer than four user labels per map on average.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"23 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":"121790204","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: 24
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