Debarshi Dutta, Aruni Roy Chowdhury, U. Bhattacharya, S. K. Parui
The wide usage of touch-screen based mobile devices has led to a large volume of the users preferring touch-based interaction with the machine, as opposed to traditional input via keyboards/mice. To exploit this, we focus on the Android platform to design a personalized handwriting recognition system that is acceptably fast, light-weight, possessing a user-friendly interface with minimally-intrusive correction and auto-personalization mechanisms. Since cursive writing on smaller screens is not usual, here we study non-cursive handwriting only. The recognition is done at character level using nearest-neighbor matching to a small, automatically user-adaptive and dynamically updating library of character-class template gestures.
{"title":"Building a Personal Handwriting Recognizer on an Android Device","authors":"Debarshi Dutta, Aruni Roy Chowdhury, U. Bhattacharya, S. K. Parui","doi":"10.1109/ICFHR.2012.189","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.189","url":null,"abstract":"The wide usage of touch-screen based mobile devices has led to a large volume of the users preferring touch-based interaction with the machine, as opposed to traditional input via keyboards/mice. To exploit this, we focus on the Android platform to design a personalized handwriting recognition system that is acceptably fast, light-weight, possessing a user-friendly interface with minimally-intrusive correction and auto-personalization mechanisms. Since cursive writing on smaller screens is not usual, here we study non-cursive handwriting only. The recognition is done at character level using nearest-neighbor matching to a small, automatically user-adaptive and dynamically updating library of character-class template gestures.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"504 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116681248","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}
Konstantinos Zagoris, I. Pratikakis, A. Antonacopoulos, B. Gatos, N. Papamarkos
In a number of types of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may be present in the same document image, giving rise to significant issues within a digitisation and recognition pipeline. It is therefore necessary to separate the two types of text before applying different recognition methodologies to each. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words paradigm (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a Support Vector Machine classifier. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with a new dataset.
{"title":"Handwritten and Machine Printed Text Separation in Document Images Using the Bag of Visual Words Paradigm","authors":"Konstantinos Zagoris, I. Pratikakis, A. Antonacopoulos, B. Gatos, N. Papamarkos","doi":"10.1109/ICFHR.2012.207","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.207","url":null,"abstract":"In a number of types of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may be present in the same document image, giving rise to significant issues within a digitisation and recognition pipeline. It is therefore necessary to separate the two types of text before applying different recognition methodologies to each. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words paradigm (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a Support Vector Machine classifier. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with a new dataset.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126138599","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}
Yutaro Iwakiri, Soma Shiraishi, Yaokai Feng, S. Uchida
This paper tackles the stroke recovery problem, which is a typical ill-posed reverse problem, by an instance-based method. The basic idea of the instance-based stroke recovery is to refer to the drawing order of a similar instance. The instance-based method has a strong merit that it can deal with multi-stroke characters and other complex characters without any special consideration. However, it requires a sufficient numbers of instances to cover those various characters. As an initial trial of the instance-based stroke recovery method, this paper describes the principle of the method and then provides several experimental results. The experimental results indicate the potential of the proposed method on recovering the drawing order of complex characters, as expected.
{"title":"On the possibility of instance-based stroke recovery","authors":"Yutaro Iwakiri, Soma Shiraishi, Yaokai Feng, S. Uchida","doi":"10.1109/ICFHR.2012.248","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.248","url":null,"abstract":"This paper tackles the stroke recovery problem, which is a typical ill-posed reverse problem, by an instance-based method. The basic idea of the instance-based stroke recovery is to refer to the drawing order of a similar instance. The instance-based method has a strong merit that it can deal with multi-stroke characters and other complex characters without any special consideration. However, it requires a sufficient numbers of instances to cover those various characters. As an initial trial of the instance-based stroke recovery method, this paper describes the principle of the method and then provides several experimental results. The experimental results indicate the potential of the proposed method on recovering the drawing order of complex characters, as expected.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123584719","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}
Manish Kumar, Zhixin Shi, S. Setlur, V. Govindaraju, R. Sitaram
An important task in Keyword Spotting in handwritten documents is to separate Keywords from Non Keywords. Very often this is achieved by learning a filler or background model. A common method of building a background model is to allow all possible sequences or transitions of characters. However, due to large variation in handwriting styles, allowing all possible sequences of characters as background might result in an increased false reject. A weak background model could result in high false accept. We propose a novel way of learning the background model dynamically. The approach first used in word spotting in speech uses a feature vector of top K local scores per character and top N global scores of matching hypotheses. A two class classifier is learned on these features to classify between Keyword and Non Keyword.
{"title":"Keyword Spotting Framework Using Dynamic Background Model","authors":"Manish Kumar, Zhixin Shi, S. Setlur, V. Govindaraju, R. Sitaram","doi":"10.1109/ICFHR.2012.223","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.223","url":null,"abstract":"An important task in Keyword Spotting in handwritten documents is to separate Keywords from Non Keywords. Very often this is achieved by learning a filler or background model. A common method of building a background model is to allow all possible sequences or transitions of characters. However, due to large variation in handwriting styles, allowing all possible sequences of characters as background might result in an increased false reject. A weak background model could result in high false accept. We propose a novel way of learning the background model dynamically. The approach first used in word spotting in speech uses a feature vector of top K local scores per character and top N global scores of matching hypotheses. A two class classifier is learned on these features to classify between Keyword and Non Keyword.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116070672","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}
Abir Chaari, Fadoua Drira, A. Alimi, Elöd Egyed-Zsigmond, Frank Lebourgeois
The cultural heritage is full of important manuscript collections preserved in digital libraries. The need to annotate and enrich the scanned documents is claimed by some users to keep traces in the system for a further use. Moreover, the reuse of annotations could help other users to accomplish repetitive tasks in a semi-automatic way. One manuscript annotation technique is the word spotting. It is a process that seeks in a document for all the fragments that are similar to the one specified by the user. The main focus of this research work is to propose a solution integrating and encapsulating the word spotting algorithm in digital libraries. This solution involves, in particular, the specification and the implementation of an architecture to integrate the image processing tool using Restful Web services. The proposed prototype is tested on the ARMARIUS digital library. This library is one of the collaborative digital archiving models that stores ancient digitized manuscripts.
{"title":"New Protocol Design for Wordspotting Assistance System: Case Study of the Collaborative Library Model - ARMARIUS","authors":"Abir Chaari, Fadoua Drira, A. Alimi, Elöd Egyed-Zsigmond, Frank Lebourgeois","doi":"10.1109/ICFHR.2012.242","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.242","url":null,"abstract":"The cultural heritage is full of important manuscript collections preserved in digital libraries. The need to annotate and enrich the scanned documents is claimed by some users to keep traces in the system for a further use. Moreover, the reuse of annotations could help other users to accomplish repetitive tasks in a semi-automatic way. One manuscript annotation technique is the word spotting. It is a process that seeks in a document for all the fragments that are similar to the one specified by the user. The main focus of this research work is to propose a solution integrating and encapsulating the word spotting algorithm in digital libraries. This solution involves, in particular, the specification and the implementation of an architecture to integrate the image processing tool using Restful Web services. The proposed prototype is tested on the ARMARIUS digital library. This library is one of the collaborative digital archiving models that stores ancient digitized manuscripts.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121998507","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 paper deals with recognition of online handwritten Bangla (Bengali) text. Here, at first, we segment cursive words into strokes. A stroke may represent a character or a part of a character. We selected a set of Bangla words written by different groups of people such that they contain all basic characters, all vowel and consonant modifiers and almost all types of possible joining among them. For segmentation of text into strokes, we discovered some rules analyzing different joining patterns of Bangla characters. Combination of online and offline information was used for segmentation. We achieved correct segmentation rate of 97.89% on the dataset. We manually analyzed different strokes to create a ground truth set of distinct stroke classes for result verification and we obtained 85 stroke classes. Directional features were used in SVM for recognition and we achieved correct stroke recognition rate of 97.68%.
{"title":"Stroke Segmentation and Recognition from Bangla Online Handwritten Text","authors":"Nilanjana Bhattacharya, U. Pal","doi":"10.1109/ICFHR.2012.275","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.275","url":null,"abstract":"This paper deals with recognition of online handwritten Bangla (Bengali) text. Here, at first, we segment cursive words into strokes. A stroke may represent a character or a part of a character. We selected a set of Bangla words written by different groups of people such that they contain all basic characters, all vowel and consonant modifiers and almost all types of possible joining among them. For segmentation of text into strokes, we discovered some rules analyzing different joining patterns of Bangla characters. Combination of online and offline information was used for segmentation. We achieved correct segmentation rate of 97.89% on the dataset. We manually analyzed different strokes to create a ground truth set of distinct stroke classes for result verification and we obtained 85 stroke classes. Directional features were used in SVM for recognition and we achieved correct stroke recognition rate of 97.68%.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129904973","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}
Forensic identification is the task of determining whether or not observed evidence arose from a known source. In every forensic domain, it is useful to determine the probability that the evidence and known can be attributed to the same individual so that identification/exclusion opinions can be accompanied by a probability statement. At present, in most forensic domains outside of DNA, it is not possible to make such a statement since the necessary probability distributions cannot be computed with reasonable accuracy. It involves determining a likelihood ratio (LR) -the ratio of the joint probability of the evidence and source under the identification hypothesis (that the evidence came from the source) and under the exclusion hypothesis (that the evidence did not arise from the source). The joint probability approach is computationally and statistically infeasible when the number of variables is even moderately large, e.g., the number of parameters to be determined is exponential with the number of variables. An approximate method is to replace the joint probability by another probability: that of (dis)imilarity between evidence and object under the two hypotheses. While this distance-based approach reduces to linear complexity with the number of variables, it is an oversimplification. A third method, which decomposes the LR into a product of two factors, one based on distance and the other on rarity, has intuitive appeal-forensic examiners assign higher importance to rare attributes in the evidence. Theoretical discussions of the three approaches and empirical evaluations done with several data types (continuous features, binary features, multinomial and graph) will be described. Experiments with handwriting using binary and multinomial features show that the distance and rarity method is significantly better than the distance only method. Work was done with Yi Tang.
{"title":"Invited Lecture II: Evaluating the Probability of Identification in the Forensic Sciences","authors":"S. Srihari","doi":"10.1109/ICFHR.2012.301","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.301","url":null,"abstract":"Forensic identification is the task of determining whether or not observed evidence arose from a known source. In every forensic domain, it is useful to determine the probability that the evidence and known can be attributed to the same individual so that identification/exclusion opinions can be accompanied by a probability statement. At present, in most forensic domains outside of DNA, it is not possible to make such a statement since the necessary probability distributions cannot be computed with reasonable accuracy. It involves determining a likelihood ratio (LR) -the ratio of the joint probability of the evidence and source under the identification hypothesis (that the evidence came from the source) and under the exclusion hypothesis (that the evidence did not arise from the source). The joint probability approach is computationally and statistically infeasible when the number of variables is even moderately large, e.g., the number of parameters to be determined is exponential with the number of variables. An approximate method is to replace the joint probability by another probability: that of (dis)imilarity between evidence and object under the two hypotheses. While this distance-based approach reduces to linear complexity with the number of variables, it is an oversimplification. A third method, which decomposes the LR into a product of two factors, one based on distance and the other on rarity, has intuitive appeal-forensic examiners assign higher importance to rare attributes in the evidence. Theoretical discussions of the three approaches and empirical evaluations done with several data types (continuous features, binary features, multinomial and graph) will be described. Experiments with handwriting using binary and multinomial features show that the distance and rarity method is significantly better than the distance only method. Work was done with Yi Tang.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129119562","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}
For better performance in multilayer or hierarchical classification of handwritten text, appropriate grouping of similar symbols is very important. Here we aim to develop a reliable grouping schema for the similar looking basic characters, numerals and vowel modifiers of Bangla language. We experimented with thickened and thinned segmented handwritten text to compare which type of image is better for which group. For classification we chose Support Vector Machine (SVM) as it outperforms other classifiers in this field. We used both “one against one” and “one against all” strategies for multiclass SVM and compared their performance.
{"title":"Grouping of Handwritten Bangla Basic Characters, Numerals and Vowel Modifiers for Multilayer Classification","authors":"Khondker Nayef Reza, Mumit Khan","doi":"10.1109/ICFHR.2012.206","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.206","url":null,"abstract":"For better performance in multilayer or hierarchical classification of handwritten text, appropriate grouping of similar symbols is very important. Here we aim to develop a reliable grouping schema for the similar looking basic characters, numerals and vowel modifiers of Bangla language. We experimented with thickened and thinned segmented handwritten text to compare which type of image is better for which group. For classification we chose Support Vector Machine (SVM) as it outperforms other classifiers in this field. We used both “one against one” and “one against all” strategies for multiclass SVM and compared their performance.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129240122","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}
Jan Stria, Martin Bresler, D. Prusa, Václav Hlaváč
This paper announces a ground truthed database of on-line handwritten mathematical formulae. It have recently been collected in our group in connection with the research on methods for structural pattern recognition. Unlike the availability of handwritten characters or texts, collections of structural objects are rather scarce, thus we would like to provide them to the community. We also present the methodology and tools used for data acquisition. Finally, we report on our experiment with the automatic generation of additional samples. The process utilizes the dataset to extract statistical descriptions of symbols alignments and relative sizes.
{"title":"MfrDB: Database of Annotated On-Line Mathematical Formulae","authors":"Jan Stria, Martin Bresler, D. Prusa, Václav Hlaváč","doi":"10.1109/ICFHR.2012.231","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.231","url":null,"abstract":"This paper announces a ground truthed database of on-line handwritten mathematical formulae. It have recently been collected in our group in connection with the research on methods for structural pattern recognition. Unlike the availability of handwritten characters or texts, collections of structural objects are rather scarce, thus we would like to provide them to the community. We also present the methodology and tools used for data acquisition. Finally, we report on our experiment with the automatic generation of additional samples. The process utilizes the dataset to extract statistical descriptions of symbols alignments and relative sizes.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116500518","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 paper fills a void in the literature of online Arabic handwritten digits recognition as no systems are dedicated to this problem. The two main contributions of this paper are introducing a large online Arabic handwritten digits dataset and developing an efficient online Arabic handwritten digits recognition system. In the dataset, we collected 30,000 online Arabic digits from 300 writers. The developed system uses a combination of temporal and spatial features to recognize those digits. The system achieved 98.73% recognition rate. Comparison with a commercial product demonstrates the superiority of the proposed system.
{"title":"Online Arabic Handwritten Digits Recognition","authors":"S. Abdelazeem, Maha El Meseery, Hany Ahmed","doi":"10.1109/ICFHR.2012.249","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.249","url":null,"abstract":"This paper fills a void in the literature of online Arabic handwritten digits recognition as no systems are dedicated to this problem. The two main contributions of this paper are introducing a large online Arabic handwritten digits dataset and developing an efficient online Arabic handwritten digits recognition system. In the dataset, we collected 30,000 online Arabic digits from 300 writers. The developed system uses a combination of temporal and spatial features to recognize those digits. The system achieved 98.73% recognition rate. Comparison with a commercial product demonstrates the superiority of the proposed system.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125254571","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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