Sketching is considered as a way to naturally express ideas during the early phases of design. For this reason, many efforts have been made to develop user interfaces and recognizers, which enable users to create sketches using pen-based devices. However, in some domains, such as in architectural and engineering fields, the drawing process turns out to be particularly tedious and time-consuming, since the symbols to be drawn may have a complex shape and recur many times in the sketches. In this paper we present a technique for symbol completion that allows users to rapidly draw diagrammatic sketches. The completion technique recovers the information on missing strokes by interacting with symbol recognizers, which are automatically generated from grammar specifications. Moreover, in order to maintain the sketch layout more familiar to the users, the added strokes are drawn according to the user drawing style.
{"title":"Using Grammar-Based Recognizers for Symbol Completion in Diagrammatic Sketches","authors":"G. Costagliola, V. Deufemia, M. Risi","doi":"10.1109/ICDAR.2007.259","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.259","url":null,"abstract":"Sketching is considered as a way to naturally express ideas during the early phases of design. For this reason, many efforts have been made to develop user interfaces and recognizers, which enable users to create sketches using pen-based devices. However, in some domains, such as in architectural and engineering fields, the drawing process turns out to be particularly tedious and time-consuming, since the symbols to be drawn may have a complex shape and recur many times in the sketches. In this paper we present a technique for symbol completion that allows users to rapidly draw diagrammatic sketches. The completion technique recovers the information on missing strokes by interacting with symbol recognizers, which are automatically generated from grammar specifications. Moreover, in order to maintain the sketch layout more familiar to the users, the added strokes are drawn according to the user drawing style.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"2 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":"131179583","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}
Lexical text correction relies on a central step where approximate search in a dictionary is used to select the best correction suggestions for an ill-formed input token. In previous work we introduced the concept of a universal Levenshtein automaton and showed how to use these automata for efficiently selecting from a dictionary all entries within a fixed Levenshtein distance to the garbled input word. In this paper we look at refinements of the basic Levenshtein distance that yield more sensible notions of similarity in distinct text correction applications, e.g. OCR. We show that the concept of a universal Levenshtein automaton can be adapted to these refinements. In this way we obtain a method for selecting correction candidates which is very efficient, at the same time selecting small candidate sets with high recall.
{"title":"Fast Selection of Small and Precise Candidate Sets from Dictionaries for Text Correction Tasks","authors":"K. Schulz, S. Mihov, Petar Mitankin","doi":"10.1109/ICDAR.2007.119","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.119","url":null,"abstract":"Lexical text correction relies on a central step where approximate search in a dictionary is used to select the best correction suggestions for an ill-formed input token. In previous work we introduced the concept of a universal Levenshtein automaton and showed how to use these automata for efficiently selecting from a dictionary all entries within a fixed Levenshtein distance to the garbled input word. In this paper we look at refinements of the basic Levenshtein distance that yield more sensible notions of similarity in distinct text correction applications, e.g. OCR. We show that the concept of a universal Levenshtein automaton can be adapted to these refinements. In this way we obtain a method for selecting correction candidates which is very efficient, at the same time selecting small candidate sets with high recall.","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":"131375563","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}
With the emergence of digital pen and paper interfaces, there is a need for gesture recognition tools for digital pen input. While there exists a variety of gesture recognition frameworks, none of them addresses the issues of supporting application developers as well as the designers of new recognition algorithms and, at the same time, can be integrated with new forms of input devices such as digital pens. We introduce iGesture, a Java-based gesture recognition framework focusing on extensibility and cross-application reusability by providing an integrated solution that includes tools for gesture recognition as well as the creation and management of gesture sets for the evaluation and optimisation of new or existing gesture recognition algorithms. In addition to traditional screen-based interaction, iGesture provides a digital pen and paper interface.
{"title":"iGesture: A General Gesture Recognition Framework","authors":"B. Signer, U. Kurmann, M. Norrie","doi":"10.1109/ICDAR.2007.139","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.139","url":null,"abstract":"With the emergence of digital pen and paper interfaces, there is a need for gesture recognition tools for digital pen input. While there exists a variety of gesture recognition frameworks, none of them addresses the issues of supporting application developers as well as the designers of new recognition algorithms and, at the same time, can be integrated with new forms of input devices such as digital pens. We introduce iGesture, a Java-based gesture recognition framework focusing on extensibility and cross-application reusability by providing an integrated solution that includes tools for gesture recognition as well as the creation and management of gesture sets for the evaluation and optimisation of new or existing gesture recognition algorithms. In addition to traditional screen-based interaction, iGesture provides a digital pen and paper interface.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"3 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":"121867685","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}
To distinguish similar characters, it is preferable to construct a classifier using a projective feature space which differentiates two similar categories. The classifier CMF has been proposed for a discriminant function, in similar characters recognition. In the CMF, a subspace is constructed by some eigenvectors, that corresponds to the smallest eigenvalues, is applied as projective feature space. A difference vector of two class-mean feature vectors are assumed as the difference between two similar categories, the CMF is constructed by projecting a feature vector onto this difference vector. In this paper, we propose new discriminant function expanding the CMF. In proposed method, we treat the Difference Subspace, which is difference between two subspaces as difference between two similar categories. The efficiency of the proposed new discriminant function has been demonstrated in similar characters recognition through extensive experiments on hand-written Japanese characters derived from the ETL9B database.
{"title":"A Classifier of Similar Characters using Compound Mahalanobis Function based on Difference Subspace","authors":"J. Hirayama, Hidehisa Nakayama, N. Kato","doi":"10.1109/ICDAR.2007.4","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.4","url":null,"abstract":"To distinguish similar characters, it is preferable to construct a classifier using a projective feature space which differentiates two similar categories. The classifier CMF has been proposed for a discriminant function, in similar characters recognition. In the CMF, a subspace is constructed by some eigenvectors, that corresponds to the smallest eigenvalues, is applied as projective feature space. A difference vector of two class-mean feature vectors are assumed as the difference between two similar categories, the CMF is constructed by projecting a feature vector onto this difference vector. In this paper, we propose new discriminant function expanding the CMF. In proposed method, we treat the Difference Subspace, which is difference between two subspaces as difference between two similar categories. The efficiency of the proposed new discriminant function has been demonstrated in similar characters recognition through extensive experiments on hand-written Japanese characters derived from the ETL9B database.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"25 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":"134579434","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 present a a statistical approach to skew detection, where the textual features of a document image are modeled as a mixture of straight lines in Gaussian noise. The EM algorithm is used to estimate the parameters of the mixture model and the skew angle estimate is extracted from the estimated parameters. Experiments prove that our method has some advantages over other existing methods in terms of accuracy and efficiency.
{"title":"An EM Based Algorithm for Skew Detection","authors":"A. Egozi, I. Dinstein, J. Chapran, M. Fairhurst","doi":"10.1109/ICDAR.2007.52","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.52","url":null,"abstract":"We present a a statistical approach to skew detection, where the textual features of a document image are modeled as a mixture of straight lines in Gaussian noise. The EM algorithm is used to estimate the parameters of the mixture model and the skew angle estimate is extracted from the estimated parameters. Experiments prove that our method has some advantages over other existing methods in terms of accuracy and efficiency.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"41 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":"115793901","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}
The effectiveness of kernel fisher discrimination analysis (KFDA) has been demonstrated by many pattern recognition applications. However, due to the large size of Gram matrix to be trained, how to use KFDA to solve large vocabulary pattern recognition task such as Chinese Characters recognition is still a challenging problem. In this paper, a two-stage KFDA approach is presented for handwritten Chinese character recognition. In the first stage, a new modified linear discriminant analysis method is developed to get the recognition candidates. In the second stage, KFDA is used to determine the final recognition result. Experiments on 1034 categories of Chinese character from 120 sets of handwriting samples shows that a 3.37% improvement of recognition rate is obtained, which suggests the effectiveness of the proposed method.
{"title":"Handwritten Chinese Character Recognition Using Modified LDA and Kernel FDA","authors":"Duanduan Yang, Lianwen Jin","doi":"10.1109/ICDAR.2007.128","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.128","url":null,"abstract":"The effectiveness of kernel fisher discrimination analysis (KFDA) has been demonstrated by many pattern recognition applications. However, due to the large size of Gram matrix to be trained, how to use KFDA to solve large vocabulary pattern recognition task such as Chinese Characters recognition is still a challenging problem. In this paper, a two-stage KFDA approach is presented for handwritten Chinese character recognition. In the first stage, a new modified linear discriminant analysis method is developed to get the recognition candidates. In the second stage, KFDA is used to determine the final recognition result. Experiments on 1034 categories of Chinese character from 120 sets of handwriting samples shows that a 3.37% improvement of recognition rate is obtained, which suggests the effectiveness of the proposed method.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"55 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":"132559338","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}
A novel approach for the Arabic handwriting recognition is presented. The use of a planar hidden Markov model (PHMM) has permitted to split the Arabic script into five homogeneous horizontal regions. Each region was described by a 1D-HMM. This modeling is based on different levels of segmentation: horizontal, natural and vertical. Both holistic and analytical approaches have been tested for the description of the median band of the Arabic writing. We show finally that a hybrid approach conducted to the improvement of the whole system performances.
{"title":"A hybrid approach for off-line Arabic handwriting recognition based on a Planar Hidden Markov modeling","authors":"Sameh Masmoudi Touj, N. Amara, H. Amiri","doi":"10.1109/ICDAR.2007.14","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.14","url":null,"abstract":"A novel approach for the Arabic handwriting recognition is presented. The use of a planar hidden Markov model (PHMM) has permitted to split the Arabic script into five homogeneous horizontal regions. Each region was described by a 1D-HMM. This modeling is based on different levels of segmentation: horizontal, natural and vertical. Both holistic and analytical approaches have been tested for the description of the median band of the Arabic writing. We show finally that a hybrid approach conducted to the improvement of the whole system performances.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"56 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":"134096675","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}
India is a multilingual multiscript country with more than 18 languages and 10 different major scripts. Not enough research work towards recognition of handwritten characters of these Indian scripts has been done. Tamil, an official as well as popular script of the southern part of India, Singapore, Malaysia, and Sri Lanka has a large character set which includes many compound characters. Only a few works towards handwriting recognition of this large character set has been reported in the literature. Recently, HP Labs India developed a database of handwritten Tamil characters. In the present paper, we describe an off-line recognition approach based on this database. The proposed method consists of two stages. In the first stage, we apply an unsupervised clustering method to create a smaller number of groups of handwritten Tamil character classes. In the second stage, we consider a supervised classification technique in each of these smaller groups for final recognition. The features considered in the two stages are different. The proposed two-stage recognition scheme provided acceptable classification accuracies on both the training and test sets of the present database.
{"title":"A Two Stage Recognition Scheme for Handwritten Tamil Characters","authors":"U. Bhattacharya, S. Ghosh, S. K. Parui","doi":"10.1109/ICDAR.2007.37","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.37","url":null,"abstract":"India is a multilingual multiscript country with more than 18 languages and 10 different major scripts. Not enough research work towards recognition of handwritten characters of these Indian scripts has been done. Tamil, an official as well as popular script of the southern part of India, Singapore, Malaysia, and Sri Lanka has a large character set which includes many compound characters. Only a few works towards handwriting recognition of this large character set has been reported in the literature. Recently, HP Labs India developed a database of handwritten Tamil characters. In the present paper, we describe an off-line recognition approach based on this database. The proposed method consists of two stages. In the first stage, we apply an unsupervised clustering method to create a smaller number of groups of handwritten Tamil character classes. In the second stage, we consider a supervised classification technique in each of these smaller groups for final recognition. The features considered in the two stages are different. The proposed two-stage recognition scheme provided acceptable classification accuracies on both the training and test sets of the present database.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"2 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":"131686054","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 Patra - an integrated document architecture which incorporates handwritten illustrations captured and rendered in a temporal fashion synchronized with audio, video, text, and image data. The architecture of Patra permits non-linear growth in the form of multiple hierarchically organized play streams. Semantic metadata is also an integral part of Patra which serves a useful purpose of organizing such documents in a collection. We have developed an email application in which the users are provided with an authoring and rendering environment to compose, view, and reply to messages in the form of Patra.
{"title":"Pàtrà: A Novel Document Architecture for Integrating Handwriting with Audio-Visual Information","authors":"Gaurav Harit, V. Mankar, S. Chaudhury","doi":"10.1109/ICDAR.2007.204","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.204","url":null,"abstract":"In this paper we present Patra - an integrated document architecture which incorporates handwritten illustrations captured and rendered in a temporal fashion synchronized with audio, video, text, and image data. The architecture of Patra permits non-linear growth in the form of multiple hierarchically organized play streams. Semantic metadata is also an integral part of Patra which serves a useful purpose of organizing such documents in a collection. We have developed an email application in which the users are provided with an authoring and rendering environment to compose, view, and reply to messages in the form of Patra.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"30 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":"132187379","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}
Hidden Markov models (HMM) have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. Even for the recognition of Oriental scripts such as Chinese, Japanese and Korean, hidden Markov models are increasingly being used to model substrokes of characters. However, when it comes to Indie script recognition, the published work employing HMMs is limited, and generally focussed on isolated character recognition. In this effort, a data-driven HMM-based online handwritten word recognition system for Tamil, an Indie script, is proposed. The accuracies obtained ranged from 98% to 92.2% with different lexicon sizes (IK to 20 K words). These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indie scripts as well.
{"title":"Hidden Markov Models for Online Handwritten Tamil Word Recognition","authors":"A. Bharath, S. Madhvanath","doi":"10.1109/ICDAR.2007.131","DOIUrl":"https://doi.org/10.1109/ICDAR.2007.131","url":null,"abstract":"Hidden Markov models (HMM) have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. Even for the recognition of Oriental scripts such as Chinese, Japanese and Korean, hidden Markov models are increasingly being used to model substrokes of characters. However, when it comes to Indie script recognition, the published work employing HMMs is limited, and generally focussed on isolated character recognition. In this effort, a data-driven HMM-based online handwritten word recognition system for Tamil, an Indie script, is proposed. The accuracies obtained ranged from 98% to 92.2% with different lexicon sizes (IK to 20 K words). These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indie scripts as well.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"63 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":"133060010","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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