Pub Date : 2002-08-06DOI: 10.1109/IWFHR.2002.1030925
N. Ayat, M. Cheriet, C. Suen
We address the problem of optimizing kernel parameters in support vector machine modeling, especially when the number of parameters is greater than one as in polynomial kernels and KMOD, our newly introduced kernel. The present work is an extended experimental study of the framework proposed by Chapelle et al. (2001) for optimizing SVM kernels using an analytic upper bound of the error. However our optimization scheme minimizes an empirical error estimate using a quasi-Newton optimization method. To assess our method, the approach is further used for adapting KMOD, RBF and polynomial kernels on synthetic data and NIST database. The method shows a much faster convergence with satisfactory results in comparison with the simple gradient descent method.
{"title":"Empirical error based optimization of SVM kernels: application to digit image recognition","authors":"N. Ayat, M. Cheriet, C. Suen","doi":"10.1109/IWFHR.2002.1030925","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030925","url":null,"abstract":"We address the problem of optimizing kernel parameters in support vector machine modeling, especially when the number of parameters is greater than one as in polynomial kernels and KMOD, our newly introduced kernel. The present work is an extended experimental study of the framework proposed by Chapelle et al. (2001) for optimizing SVM kernels using an analytic upper bound of the error. However our optimization scheme minimizes an empirical error estimate using a quasi-Newton optimization method. To assess our method, the approach is further used for adapting KMOD, RBF and polynomial kernels on synthetic data and NIST database. The method shows a much faster convergence with satisfactory results in comparison with the simple gradient descent method.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125802046","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 : 2002-08-06DOI: 10.1109/IWFHR.2002.1030895
Qizhi Xu, Jinho Kim, L. Lam, C. Suen
This paper describes an off-line system which recognizes unconstrained handwritten month words extracted from Canadian bank cheques. A segmentation based grapheme level HMM (hidden Markov model) classifier and two multilayer perceptron classifiers with different architectures and different features have been developed in CENPARMI for the recognition of month words. In this paper, a combination method with an effective conditional topology is presented, and the most widely used combination rules including Vote, Sum and Product, are experimented. A new modified Product rule is also proposed, which has produced the best recognition rate of 85.36% when tested on a real-life standard Canadian bank cheque database.
{"title":"Recognition of handwritten month words on bank cheques","authors":"Qizhi Xu, Jinho Kim, L. Lam, C. Suen","doi":"10.1109/IWFHR.2002.1030895","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030895","url":null,"abstract":"This paper describes an off-line system which recognizes unconstrained handwritten month words extracted from Canadian bank cheques. A segmentation based grapheme level HMM (hidden Markov model) classifier and two multilayer perceptron classifiers with different architectures and different features have been developed in CENPARMI for the recognition of month words. In this paper, a combination method with an effective conditional topology is presented, and the most widely used combination rules including Vote, Sum and Product, are experimented. A new modified Product rule is also proposed, which has produced the best recognition rate of 85.36% when tested on a real-life standard Canadian bank cheque database.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124674020","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 : 2002-08-06DOI: 10.1109/IWFHR.2002.1030950
L. M. Mestetskii, I. Reyer, T. Sederberg
A new approach to the segmentation of handwritten text is presented that is based on approximating a binary raster image with a set of polygons and building a continuous skeleton of those polygons. Polygons and skeletons are then used in extraction of lines, removing of spots and artifacts, extraction of words from lines and extraction of strokes from words.
{"title":"Continuous approach to segmentation of handwritten text","authors":"L. M. Mestetskii, I. Reyer, T. Sederberg","doi":"10.1109/IWFHR.2002.1030950","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030950","url":null,"abstract":"A new approach to the segmentation of handwritten text is presented that is based on approximating a binary raster image with a set of polygons and building a continuous skeleton of those polygons. Polygons and skeletons are then used in extraction of lines, removing of spots and artifacts, extraction of words from lines and extraction of strokes from words.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130114627","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 : 2002-08-06DOI: 10.1109/IWFHR.2002.1030915
Gregory F. Russell, M. Perrone, Yi-Min Chee, A. Ziq
This paper investigates the use of both typed and handwritten queries to retrieve handwritten documents. The recognition-based approach reported here is novel in that it expands documents in a fashion analogous to query expansion: Individual documents are expanded using N-best lists which embody additional statistical information from a hidden Markov model (HMM) based handwriting recognizer used to transcribe each of the handwritten documents. This additional information enables the retrieval methods to be robust to machine transcription errors, retrieving documents which otherwise would be unretrievable. Cross-writer experiments on a database of 10985 words in 108 documents from 108 writers, and within-writer experiments in a probabilistic framework, on a database of 537724 words in 3342 documents from 43 writers, indicate that significant improvements in retrieval performance can be achieved. The second database is the largest database of on-line handwritten documents known to its.
{"title":"Handwritten document retrieval","authors":"Gregory F. Russell, M. Perrone, Yi-Min Chee, A. Ziq","doi":"10.1109/IWFHR.2002.1030915","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030915","url":null,"abstract":"This paper investigates the use of both typed and handwritten queries to retrieve handwritten documents. The recognition-based approach reported here is novel in that it expands documents in a fashion analogous to query expansion: Individual documents are expanded using N-best lists which embody additional statistical information from a hidden Markov model (HMM) based handwriting recognizer used to transcribe each of the handwritten documents. This additional information enables the retrieval methods to be robust to machine transcription errors, retrieving documents which otherwise would be unretrievable. Cross-writer experiments on a database of 10985 words in 108 documents from 108 writers, and within-writer experiments in a probabilistic framework, on a database of 537724 words in 3342 documents from 43 writers, indicate that significant improvements in retrieval performance can be achieved. The second database is the largest database of on-line handwritten documents known to its.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"26 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132359741","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 : 2002-08-06DOI: 10.1109/IWFHR.2002.1030960
Masahiro Tanaka, Yumi Ishino, Hironori Shimada, T. Inoue
This paper proposes the point-wise matching method which is to be used as the pre-processing in online signature verification using only the positional information. After this pre-processing, various dynamical or local features of the signature can be used in verification. The test signature and the model one are to be matched point-wise by applying time-variant linear transformation. Kalman filter and the smoother are used for estimating the time-variant transformation parameters. Numerical experiment shows quite a good performance for real online signatures.
{"title":"DP matching using Kalman filter as pre-processing in on-line signature verification","authors":"Masahiro Tanaka, Yumi Ishino, Hironori Shimada, T. Inoue","doi":"10.1109/IWFHR.2002.1030960","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030960","url":null,"abstract":"This paper proposes the point-wise matching method which is to be used as the pre-processing in online signature verification using only the positional information. After this pre-processing, various dynamical or local features of the signature can be used in verification. The test signature and the model one are to be matched point-wise by applying time-variant linear transformation. Kalman filter and the smoother are used for estimating the time-variant transformation parameters. Numerical experiment shows quite a good performance for real online signatures.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121278696","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 : 2002-08-06DOI: 10.1109/IWFHR.2002.1030938
Matthias Zimmermann, H. Bunke
This paper investigates the use of three different schemes to optimize the number of states of linear left-to-right hidden Markov models (HMM). In the first method, we describe the fixed length modeling scheme where each character model is assigned the same number of states. The second method considered is the Bakis length modeling where the number of model states is set to a given fraction of the average number of observations of the corresponding character. In the third modeling scheme the number of model states is set to a specified quantile of the corresponding character length histogram. This method is called quantile length modeling. A comparison of different length modeling schemes was carried out with a handwriting recognition system using off-line images of cursively handwritten English words from the IAM database. For the fixed length modeling, a recognition rate of 61% was achieved. Using the Bakis or quantile length modeling the word recognition rates could be improved to over 69%.
{"title":"Hidden Markov model length optimization for handwriting recognition systems","authors":"Matthias Zimmermann, H. Bunke","doi":"10.1109/IWFHR.2002.1030938","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030938","url":null,"abstract":"This paper investigates the use of three different schemes to optimize the number of states of linear left-to-right hidden Markov models (HMM). In the first method, we describe the fixed length modeling scheme where each character model is assigned the same number of states. The second method considered is the Bakis length modeling where the number of model states is set to a given fraction of the average number of observations of the corresponding character. In the third modeling scheme the number of model states is set to a specified quantile of the corresponding character length histogram. This method is called quantile length modeling. A comparison of different length modeling schemes was carried out with a handwriting recognition system using off-line images of cursively handwritten English words from the IAM database. For the fixed length modeling, a recognition rate of 61% was achieved. Using the Bakis or quantile length modeling the word recognition rates could be improved to over 69%.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"388 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351977","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 : 2002-08-06DOI: 10.1109/IWFHR.2002.1030893
Alessandro Lameiras Koerich, Yann Leydier, R. Sabourin, C. Suen
We present a hybrid recognition system that integrates hidden Markov models (HMM) with neural networks (NN) in a probabilistic framework. The input data is processed first by a lexicon-driven word recognizer based on HMMs to generate a list of the candidate N-best-scoring word hypotheses as well as the segmentation of such word hypotheses into characters. An NN classifier is used to generate a score for each segmented character and in the end, the scores from the HMM and the NN classifiers are combined to optimize performance. Experimental results show that for an 80,000-word vocabulary, the hybrid HMM/NN system improves by about 10% the word recognition rate over the HMM system alone.
{"title":"A hybrid large vocabulary handwritten word recognition system using neural networks with hidden Markov models","authors":"Alessandro Lameiras Koerich, Yann Leydier, R. Sabourin, C. Suen","doi":"10.1109/IWFHR.2002.1030893","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030893","url":null,"abstract":"We present a hybrid recognition system that integrates hidden Markov models (HMM) with neural networks (NN) in a probabilistic framework. The input data is processed first by a lexicon-driven word recognizer based on HMMs to generate a list of the candidate N-best-scoring word hypotheses as well as the segmentation of such word hypotheses into characters. An NN classifier is used to generate a score for each segmented character and in the end, the scores from the HMM and the NN classifiers are combined to optimize performance. Experimental results show that for an 80,000-word vocabulary, the hybrid HMM/NN system improves by about 10% the word recognition rate over the HMM system alone.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"79 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131879726","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 : 2002-08-06DOI: 10.1109/IWFHR.2002.1030961
J. Allan, Tony Allen, N. Sherkat
This paper introduces a novel approach for the automatic assessment of children's responses to standardised English exam questions. The constrained nature of the question and answer medium is exploited to produce an automatic assessment mechanism that is both highly accurate and produces a reasonable level of response yield. It is shown that the novel approach can achieve 100% scoring accuracy on 44% of all responses compared to a traditional lexical approach that has an error rate of 41%. When a thresholding method, similar to that used in the novel approach is applied, the traditional approach can achieve an accuracy of 100% but with a response yield of only 5%. The approach introduced in this paper is thus shown to have a significant advantage over the traditional lexical based assessment.
{"title":"Confident assessment of children's handwritten responses","authors":"J. Allan, Tony Allen, N. Sherkat","doi":"10.1109/IWFHR.2002.1030961","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030961","url":null,"abstract":"This paper introduces a novel approach for the automatic assessment of children's responses to standardised English exam questions. The constrained nature of the question and answer medium is exploited to produce an automatic assessment mechanism that is both highly accurate and produces a reasonable level of response yield. It is shown that the novel approach can achieve 100% scoring accuracy on 44% of all responses compared to a traditional lexical approach that has an error rate of 41%. When a thresholding method, similar to that used in the novel approach is applied, the traditional approach can achieve an accuracy of 100% but with a response yield of only 5%. The approach introduced in this paper is thus shown to have a significant advantage over the traditional lexical based assessment.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134161727","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 : 2002-08-06DOI: 10.1109/IWFHR.2002.1030877
Bin Zhang, S. Srihari
Fast nearest neighbor (NN) finding has been extensively studied. While some fast NN algorithms using metrics rely on the essential properties of metric spaces, the others using non-metric measures fail for large-size templates. However in some applications with very large size templates, the best performance is achieved by NN methods based on the dissimilarity measures resulting in a special space where computations cannot be pruned by the algorithms based-on the triangular inequality. For such NN methods, the existing fast algorithms except condensing algorithms are not applicable. In this paper, a fast hierarchical search algorithm is proposed to find k-NNs using a non-metric measure in a binary feature space. Experiments with handwritten digit recognition show that the new algorithm reduces on average dissimilarity computations by more than 90% while losing the accuracy by less than 0.1%, with a 10% increase in memory.
{"title":"A fast algorithm for finding k-nearest neighbors with non-metric dissimilarity","authors":"Bin Zhang, S. Srihari","doi":"10.1109/IWFHR.2002.1030877","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030877","url":null,"abstract":"Fast nearest neighbor (NN) finding has been extensively studied. While some fast NN algorithms using metrics rely on the essential properties of metric spaces, the others using non-metric measures fail for large-size templates. However in some applications with very large size templates, the best performance is achieved by NN methods based on the dissimilarity measures resulting in a special space where computations cannot be pruned by the algorithms based-on the triangular inequality. For such NN methods, the existing fast algorithms except condensing algorithms are not applicable. In this paper, a fast hierarchical search algorithm is proposed to find k-NNs using a non-metric measure in a binary feature space. Experiments with handwritten digit recognition show that the new algorithm reduces on average dissimilarity computations by more than 90% while losing the accuracy by less than 0.1%, with a 10% increase in memory.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132315921","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 : 2002-08-06DOI: 10.1109/IWFHR.2002.1030928
S. Kanoun, A. Ennaji, Y. Lecourtier, A. Alimi
A method for Arabic and Latin text block differentiation for printed and handwritten scripts is proposed. This method is based on a morphological analysis for each script at the text block level and a geometrical analysis at the line and the connected component level. In this paper, we present a brief survey, of existing methods used for scripts differentiation as well as a general characteristics of Arabic and Latin scripts. Then, We describe our method for the differentiation of these last scripts. We finally show two experimental results on two different data sets. 400 text blocks constitute the first one and 335 text blocks compose the second.
{"title":"Script and nature differentiation for Arabic and Latin text images","authors":"S. Kanoun, A. Ennaji, Y. Lecourtier, A. Alimi","doi":"10.1109/IWFHR.2002.1030928","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030928","url":null,"abstract":"A method for Arabic and Latin text block differentiation for printed and handwritten scripts is proposed. This method is based on a morphological analysis for each script at the text block level and a geometrical analysis at the line and the connected component level. In this paper, we present a brief survey, of existing methods used for scripts differentiation as well as a general characteristics of Arabic and Latin scripts. Then, We describe our method for the differentiation of these last scripts. We finally show two experimental results on two different data sets. 400 text blocks constitute the first one and 335 text blocks compose the second.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114736584","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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