Pub Date : 2002-08-06DOI: 10.1109/IWFHR.2002.1030876
U. Miletzki
Jurgen Schurmann died on January 19, 2001, much too early, at the age of 66 years. He was a grand pioneer in the field of pattern recognition and a tireless source and trigger of sophisticated theoretical ideas and their transformations into high performance recognition products which are spread all over the world. For this reason, the programme committee of the 2002 "International Workshop on Frontiers of Handwriting Recognition" (IWFHR-8) has decided to dedicate this event to this extraordinary senior scientist of pattern recognition. In honour and in memory of this vivid, dynamic and creative man, we want to reflect the essence of his oeuvre; gained during a life-long quest to find a way - as he would put it - "from pixel to meaning". This paper is focused on the following questions: Who was this man? What were his scientific roots? What was his basic contribution? What are the offsprings of his work? What impetus did he give to the scientific community?.
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Pub Date : 2002-08-06DOI: 10.1109/IWFHR.2002.1030898
S. Maddouri, H. Amiri, A. Belaïd, Christophe Choisy
We propose an Arabic handwritten word recognition system based on the idea of the PERCEPTRO system developed by Cote (Cote et al. (1998)) for Latin word recognition. It is a specific neural network, named transparent neural network, combining a global and a local vision modeling (GVM-LVM) of the word. In the forward propagation movement, the former (GVM) proposes a list of structural features characterizing the presence of some letters in the word. GVM proposes a list of possible letters and words containing these characteristics. Then, in the backpropagation movement, these letters are confirmed or not according to their proximity with corresponding printed letters. The correspondence between the letter shapes and the corresponding printed letters is performed by LVM using the correspondence of their Fourier descriptors, playing the role of a letter shape normalizer.
基于Cote (Cote et al.(1998))开发的用于拉丁单词识别的PERCEPTRO系统的思想，我们提出了一个阿拉伯手写单词识别系统。它是一种特定的神经网络，命名为透明神经网络，结合了全局和局部视觉建模(GVM-LVM)的词。在正向传播运动中，前者(GVM)提出了一组表征单词中某些字母存在的结构特征。GVM提出了包含这些特征的可能字母和单词的列表。然后，在反向传播运动中，根据这些字母与相应印刷字母的接近程度来确认或不确认这些字母。LVM利用其傅里叶描述子的对应性来实现字母形状与相应印刷字母之间的对应关系，起到字母形状归一化器的作用。
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Pub Date : 2002-08-06DOI: 10.1109/IWFHR.2002.1030934
V. Vuori
This work shows how a self-organizing map (SOM) can be applied in the analysis of different handwriting styles. The handwriting samples analyzed have been collected in online fashion with special writing equipments such as pressure sensitive tablets. The handwriting style of an individual subject is represented by a vector components of which reflect the tendencies of the writer to use certain prototypical styles for isolated alphanumeric characters. This study shows that correlations between different writing styles, both character-wise and writer-wise can be found. Clusters of different personal writing styles can be found by studying the U-matrix visualization of the SOM trained with data collected from over 700 subjects. An examination of the component planes of the SOM reveals some interesting correlations between the prototypical character styles.
{"title":"Clustering writing styles with a self-organizing map","authors":"V. Vuori","doi":"10.1109/IWFHR.2002.1030934","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030934","url":null,"abstract":"This work shows how a self-organizing map (SOM) can be applied in the analysis of different handwriting styles. The handwriting samples analyzed have been collected in online fashion with special writing equipments such as pressure sensitive tablets. The handwriting style of an individual subject is represented by a vector components of which reflect the tendencies of the writer to use certain prototypical styles for isolated alphanumeric characters. This study shows that correlations between different writing styles, both character-wise and writer-wise can be found. Clusters of different personal writing styles can be found by studying the U-matrix visualization of the SOM trained with data collected from over 700 subjects. An examination of the component planes of the SOM reveals some interesting correlations between the prototypical character styles.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"55 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":"122575433","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.2002.1030899
M. Schüßler
In this paper we present a system for automating parameter optimization of pattern recognition systems and demonstrate its capabilities on script word recognition systems. This system, called Optima (optimization manager) has been specially developed to fit the requirements of optimization in the pattern recognition field, where computation- and engineering-costs for system evaluation are very high. Our experiments show that automatic parameter optimization not only performs the task as well as the experienced engineers, thereby relieving them from routine work, but ultimately also outperforms hand-tuning in terms of system performance.
{"title":"Automating performance optimization for script word recognition systems","authors":"M. Schüßler","doi":"10.1109/IWFHR.2002.1030899","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030899","url":null,"abstract":"In this paper we present a system for automating parameter optimization of pattern recognition systems and demonstrate its capabilities on script word recognition systems. This system, called Optima (optimization manager) has been specially developed to fit the requirements of optimization in the pattern recognition field, where computation- and engineering-costs for system evaluation are very high. Our experiments show that automatic parameter optimization not only performs the task as well as the experienced engineers, thereby relieving them from routine work, but ultimately also outperforms hand-tuning in terms of system performance.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"17 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":"123514135","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.2002.1030884
K. Maruyama, M. Maruyama, H. Miyao, Y. Nakano
Describes a method to improve the cumulative recognition rates of pattern recognition using a decision directed acyclic graph (DDAG) based on support vector machines (SVM). Though the original DDAG has high level of performance and its execution speed is fast, it does not consider the so-called cumulative recognition rate. We construct a DDAG which can incorporate the cumulative recognition rate. As a result of our experiment for handprinted Hiragana characters in JEITA-HP, the cumulative recognition rate is improved and its execution time is almost the same as the original DDAG and 30 times faster than the Max Win Algorithm which is one of the famous recognition methods using support vector machines for a multi-class problem.
{"title":"Handprinted Hiragana recognition using support vector machines","authors":"K. Maruyama, M. Maruyama, H. Miyao, Y. Nakano","doi":"10.1109/IWFHR.2002.1030884","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030884","url":null,"abstract":"Describes a method to improve the cumulative recognition rates of pattern recognition using a decision directed acyclic graph (DDAG) based on support vector machines (SVM). Though the original DDAG has high level of performance and its execution speed is fast, it does not consider the so-called cumulative recognition rate. We construct a DDAG which can incorporate the cumulative recognition rate. As a result of our experiment for handprinted Hiragana characters in JEITA-HP, the cumulative recognition rate is improved and its execution time is almost the same as the original DDAG and 30 times faster than the Max Win Algorithm which is one of the famous recognition methods using support vector machines for a multi-class problem.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"21 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":"126731021","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.2002.1030881
H. Rowley, Manish Goyal, John Bennett
Much research in handwriting recognition has focused on how to improve recognizers with constrained training set sizes. This paper presents the results of training a nearest-neighbor based online Japanese Kanji recognizer and a neural-network based online cursive English recognizer on a wide range of training set sizes, including sizes not generally available. The experiments demonstrate that increasing the amount of training data improves the accuracy, even when the recognizer's representation power is limited.
{"title":"The effect of large training set sizes on online Japanese Kanji and English cursive recognizers","authors":"H. Rowley, Manish Goyal, John Bennett","doi":"10.1109/IWFHR.2002.1030881","DOIUrl":"https://doi.org/10.1109/IWFHR.2002.1030881","url":null,"abstract":"Much research in handwriting recognition has focused on how to improve recognizers with constrained training set sizes. This paper presents the results of training a nearest-neighbor based online Japanese Kanji recognizer and a neural-network based online cursive English recognizer on a wide range of training set sizes, including sizes not generally available. The experiments demonstrate that increasing the amount of training data improves the accuracy, even when the recognizer's representation power is limited.","PeriodicalId":114017,"journal":{"name":"Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition","volume":"42 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114006739","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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