A recognition system based on template matching for identifying handwritten Farsi/Arabic numerals has been developed in this paper. Template matching is a fundamental method of detecting the presence of objects and identifying them in an image. In the proposed method, templates have been chosen so that they represent the features of FARSI/Arabic prescribed form of writing as possible. Experimental results show that the performance of proposed language-based method has been achieved more than the other usual common feature extraction approaches. NM-MLP is used as a classifier and trained with 6000 samples. Test set includes 4000 samples. The recognition rate of 97.65% was obtained, which is 0.64% more than Zernike moment approach.
{"title":"Language-Based Feature Extraction Using Template-Matching in Farsi/Arabic Handwritten Numeral Recognition","authors":"M. Ziaratban, K. Faez, F. Faradji","doi":"10.1109/ICDAR.2007.273","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.273","url":null,"abstract":"A recognition system based on template matching for identifying handwritten Farsi/Arabic numerals has been developed in this paper. Template matching is a fundamental method of detecting the presence of objects and identifying them in an image. In the proposed method, templates have been chosen so that they represent the features of FARSI/Arabic prescribed form of writing as possible. Experimental results show that the performance of proposed language-based method has been achieved more than the other usual common feature extraction approaches. NM-MLP is used as a classifier and trained with 6000 samples. Test set includes 4000 samples. The recognition rate of 97.65% was obtained, which is 0.64% more than Zernike moment approach.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114902115","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}
Pub Date : 2007-11-12DOI: 10.1109/ICDAR.2007.4378669
T. Nakai, K. Kise, M. Iwamura
Annotations on paper documents include important information. We can exploit the information by extracting and analyzing annotations. In this paper, we propose a method of annotation extraction from paper documents. Unlike previous methods which limit colors or types of annotations to be extracted, the proposed method attempts to extract annotations by comparing a document image of an annotated document with its original document image for removing the limitations. The proposed method is characterized by fast matching and flexible subtraction of images both of which are essential to the annotation extraction by comparison. Experimental results have shown that color annotations can be extracted from color documents.
{"title":"A Method of Annotation Extraction from Paper Documents Using Alignment Based on Local Arrangements of Feature Points","authors":"T. Nakai, K. Kise, M. Iwamura","doi":"10.1109/ICDAR.2007.4378669","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.4378669","url":null,"abstract":"Annotations on paper documents include important information. We can exploit the information by extracting and analyzing annotations. In this paper, we propose a method of annotation extraction from paper documents. Unlike previous methods which limit colors or types of annotations to be extracted, the proposed method attempts to extract annotations by comparing a document image of an annotated document with its original document image for removing the limitations. The proposed method is characterized by fast matching and flexible subtraction of images both of which are essential to the annotation extraction by comparison. Experimental results have shown that color annotations can be extracted from color documents.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116847547","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}
Shading distortions are often perceived in geometrically distorted document images due to the change of surface normal with respect to the illumination direction. Such distortions are undesirable because they hamper OCR performance tremendously even when the geometric distortions are corrected. In this paper, we propose an effective method that removes shading distortions in images of documents with various geometric shapes based on the notion of intrinsic images. We first try to derive the shading image using an inpainting technique with an automatic mask generation routine and then apply a surface fitting procedure with radial basis functions to remove pepper noises in the inpainted image and return a smooth shading image. Once the shading image is extracted, the reflectance image can be obtained automatically. Experiments on a wide range of distorted document images demonstrate a robust performance. Moreover, we also show its potential applications to the restoration of historical handwritten documents.
{"title":"Removing Shading Distortions in Camera-based Document Images Using Inpainting and Surface Fitting With Radial Basis Functions","authors":"Li Zhang, A. Yip, C. Tan","doi":"10.1109/ICDAR.2007.217","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.217","url":null,"abstract":"Shading distortions are often perceived in geometrically distorted document images due to the change of surface normal with respect to the illumination direction. Such distortions are undesirable because they hamper OCR performance tremendously even when the geometric distortions are corrected. In this paper, we propose an effective method that removes shading distortions in images of documents with various geometric shapes based on the notion of intrinsic images. We first try to derive the shading image using an inpainting technique with an automatic mask generation routine and then apply a surface fitting procedure with radial basis functions to remove pepper noises in the inpainted image and return a smooth shading image. Once the shading image is extracted, the reflectance image can be obtained automatically. Experiments on a wide range of distorted document images demonstrate a robust performance. Moreover, we also show its potential applications to the restoration of historical handwritten documents.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115424548","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}
In this article, we present initially arguments supporting the idea of the approximation of a cursive handwriting trajectory by arcs of ellipses. Then, we introduce a new strategy which improves the dynamic and geometrical features of the online handwritten trajectory modeling. We show that the curvilinear velocity can be rebuilt with the superposition of two components successively named the "Beta" model and the "Carrying" dragged component. After that, we integrated the geometrical characteristics as the arcs of ellipses for the layout modeling.
{"title":"New Strategy for the On-Line Handwriting Modelling","authors":"H. Boubaker, M. Kherallah, A. Alimi","doi":"10.1109/ICDAR.2007.177","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.177","url":null,"abstract":"In this article, we present initially arguments supporting the idea of the approximation of a cursive handwriting trajectory by arcs of ellipses. Then, we introduce a new strategy which improves the dynamic and geometrical features of the online handwritten trajectory modeling. We show that the curvilinear velocity can be rebuilt with the superposition of two components successively named the \"Beta\" model and the \"Carrying\" dragged component. After that, we integrated the geometrical characteristics as the arcs of ellipses for the layout modeling.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"54 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120814857","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}
Aurélie Lemaitre, J. Camillerapp, Bertrand Coüasnon
When reading a document, we intuitively have a first global approach in order to determine the whole structure, before reading parts in details. We propose to apply the same kind of mechanism by introducing the concept of multiresolution in an existing generic method for structured document recognition. This new combination of different vision levels makes it possible to recognize low structured documents. We present our work on an example: the multiresolution description of archive documents that are naturalization decree registers from the 19th and 20th century. The validation has been made on 85,088 images. Integrated in a platform for archive documents, the located elements offers to users a fast leaf through naturalization decrees.
{"title":"Contribution of Multiresolution Description for Archive Document Structure Recognition","authors":"Aurélie Lemaitre, J. Camillerapp, Bertrand Coüasnon","doi":"10.1109/ICDAR.2007.92","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.92","url":null,"abstract":"When reading a document, we intuitively have a first global approach in order to determine the whole structure, before reading parts in details. We propose to apply the same kind of mechanism by introducing the concept of multiresolution in an existing generic method for structured document recognition. This new combination of different vision levels makes it possible to recognize low structured documents. We present our work on an example: the multiresolution description of archive documents that are naturalization decree registers from the 19th and 20th century. The validation has been made on 85,088 images. Integrated in a platform for archive documents, the located elements offers to users a fast leaf through naturalization decrees.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121105775","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}
In this paper, we propose a classifier fusion strategy which trains MQDF (modified quadratic discriminant functions) classifiers using cascade structure and combines classifiers on the measurement level to improve handwritten character recognition performance. The generalized confidence is introduced to compute recognition score, and the maximum rule based fusion is applied. The proposed fusion strategy is practical and effective. Its performance is evaluated by handwritten Chinese character recognition experiments on different databases. Experimental results show that the proposed algorithm achieves at least 10% reduction on classification error, and even higher 24% classification error reduction on bad quality samples.
{"title":"An Effective and Practical Classifier Fusion Strategy for Improving Handwritten Character Recognition","authors":"Qiang Fu, X. Ding, T. Li, C. Liu","doi":"10.1109/ICDAR.2007.48","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.48","url":null,"abstract":"In this paper, we propose a classifier fusion strategy which trains MQDF (modified quadratic discriminant functions) classifiers using cascade structure and combines classifiers on the measurement level to improve handwritten character recognition performance. The generalized confidence is introduced to compute recognition score, and the maximum rule based fusion is applied. The proposed fusion strategy is practical and effective. Its performance is evaluated by handwritten Chinese character recognition experiments on different databases. Experimental results show that the proposed algorithm achieves at least 10% reduction on classification error, and even higher 24% classification error reduction on bad quality samples.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121259293","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}
This work presents an effective method for writer identification in handwritten documents. We have developed a local approach, based on the extraction of characteristics that are specific to a writer. To exploit the existence of redundant patterns within a handwriting, the writing is divided into a large number of small sub-images, and the sub-images that are morphologically similar are grouped together in the same classes. The patterns, which occur frequently for a writer are thus extracted. The author of the unknown document is then identified by a Bayesian classifier. The system trained and tested on 50 documents of the same number of authors, reported an identification rate of 94%.
{"title":"Writer Identification in Handwritten Documents","authors":"I. Siddiqi, N. Vincent","doi":"10.1109/ICDAR.2007.270","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.270","url":null,"abstract":"This work presents an effective method for writer identification in handwritten documents. We have developed a local approach, based on the extraction of characteristics that are specific to a writer. To exploit the existence of redundant patterns within a handwriting, the writing is divided into a large number of small sub-images, and the sub-images that are morphologically similar are grouped together in the same classes. The patterns, which occur frequently for a writer are thus extracted. The author of the unknown document is then identified by a Bayesian classifier. The system trained and tested on 50 documents of the same number of authors, reported an identification rate of 94%.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127360143","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}
In this paper we present a method to locate and recognize graphical symbols appearing in real images. A vectorial signature is defined to describe graphical symbols. It is formulated in terms of accumulated length and angular information computed from polygonal approximation of contours. The proposed method aims to locate and recognize graphical symbols in cluttered environments at the same time, without needing a segmentation step. The symbol signature is tolerant to rotation, scale, translation and to distortions such as weak perspective, blurring effect and illumination changes usually present when working with scenes acquired with low resolution cameras in open environments.
{"title":"Camera-Based Graphical Symbol Detection","authors":"Marçal Rusiñol, J. Lladós, P. Dosch","doi":"10.1109/ICDAR.2007.76","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.76","url":null,"abstract":"In this paper we present a method to locate and recognize graphical symbols appearing in real images. A vectorial signature is defined to describe graphical symbols. It is formulated in terms of accumulated length and angular information computed from polygonal approximation of contours. The proposed method aims to locate and recognize graphical symbols in cluttered environments at the same time, without needing a segmentation step. The symbol signature is tolerant to rotation, scale, translation and to distortions such as weak perspective, blurring effect and illumination changes usually present when working with scenes acquired with low resolution cameras in open environments.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127493826","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}
Documents scanned by sheet-fed scanners often exhibit distortions due to the feeding and scanning mechanism. This paper presents a new model, motivated by the distortions observed in such documents. Numerical problems affecting the use of this model are addressed using an approximated model which is easier to estimate correctly. We demonstrate results showing the robustness and accuracy of this model on sheet-fed scanners output, and relate to existing techniques for registration and drop-out of structured forms.
{"title":"A New Physically Motivated Warping Model for Form Drop-Out","authors":"G. Rosman, A. Tzadok, D. Tal","doi":"10.1109/ICDAR.2007.25","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.25","url":null,"abstract":"Documents scanned by sheet-fed scanners often exhibit distortions due to the feeding and scanning mechanism. This paper presents a new model, motivated by the distortions observed in such documents. Numerical problems affecting the use of this model are addressed using an approximated model which is easier to estimate correctly. We demonstrate results showing the robustness and accuracy of this model on sheet-fed scanners output, and relate to existing techniques for registration and drop-out of structured forms.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125898529","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}
We address document image classification by visual appearance. An image is represented by a variable-length list of visually salient features. A hierarchical Bayesian network is used to model the joint density of these features. This model promotes generalization from a few samples by sharing component probability distributions among different categories, and by factoring out a common displacement vector shared by all features within an image. The Bayesian network is implemented as a factor graph, and parameter estimation and inference are both done by loopy belief propagation. We explain and illustrate our model on a simple shape classification task. We obtain close to 90% accuracy on classifying journal articles from memos in the UWASH-II dataset, as well as on other classification tasks on a home-grown data set of technical articles.
{"title":"A Shared Parts Model for Document Image Recognition","authors":"Mithun Das Gupta, Prateek Sarkar","doi":"10.1109/ICDAR.2007.34","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.34","url":null,"abstract":"We address document image classification by visual appearance. An image is represented by a variable-length list of visually salient features. A hierarchical Bayesian network is used to model the joint density of these features. This model promotes generalization from a few samples by sharing component probability distributions among different categories, and by factoring out a common displacement vector shared by all features within an image. The Bayesian network is implemented as a factor graph, and parameter estimation and inference are both done by loopy belief propagation. We explain and illustrate our model on a simple shape classification task. We obtain close to 90% accuracy on classifying journal articles from memos in the UWASH-II dataset, as well as on other classification tasks on a home-grown data set of technical articles.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126080192","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}
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