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Identifying Script on WordLevel with Informational Confidence
"... In this paper, we present a multiple classifier system for script identification. Applying a Gabor filter analysis of textures on wordlevel, our system identifies Latin and nonLatin words in bilingual printed documents. The classifier system comprises four different architectures based on nearest ..."
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Cited by 16 (6 self)
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In this paper, we present a multiple classifier system for script identification. Applying a Gabor filter analysis of textures on wordlevel, our system identifies Latin and nonLatin words in bilingual printed documents. The classifier system comprises four different architectures based on nearest neighbors, weighted Euclidean distances, Gaussian mixture models, and support vector machines. We report results for Arabic, Chinese, Hindi, and Korean script. Moreover, we show that combining informational confidence values using sumrule can consistently outperform the best single recognition rate. 1.
Review of classifier combination methods
 In Machine Learning in Document Analysis and Recognition. Informatica 34 (2010) 111–118 S. Vemulapalli et al
, 2008
"... Summary. Classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. In this chapter we review and categorize major advancements in this field. Despite a significant number of publications describing successful classifier combin ..."
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Cited by 11 (2 self)
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Summary. Classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. In this chapter we review and categorize major advancements in this field. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still missing and achieved improvements are inconsistent. By introducing different categories of classifier combinations in this review we attempt to put forward more specific directions for future theoretical research. We also introduce a retraining effect and effects of locality based training as important properties of classifier combinations. Such effects have significant influence on the performance of combinations, and their study is necessary for complete theoretical understanding of combination algorithms. 1
Using Informational Confidence Values for Classifier Combination: An Experiment with Combined OnLine/OffLine Japanese Character Recognition
 In: Proc. of the 9th Int. Workshop on Frontiers in Handwriting Recognition
, 2004
"... Classifier combination has turned out to be a powerful tool for achieving high recognition rates, especially in fields where the development of a powerful single classifier system requires considerable efforts. However, the intensive investigation of multiple classifier systems has not resulted in a ..."
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Cited by 5 (5 self)
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Classifier combination has turned out to be a powerful tool for achieving high recognition rates, especially in fields where the development of a powerful single classifier system requires considerable efforts. However, the intensive investigation of multiple classifier systems has not resulted in a convincing theoretical foundation yet. Lacking proper mathematical concepts, many systems still use empirical heuristics and ad hoc combination schemes. My paper presents an informationtheoretical framework for combining confidence values generated by different classifiers. The main idea is to normalize each confidence value in such a way that it equals its informational content. Based on Shannon’s notion of information, I measure information by means of a performance function that estimates the classification performance for each confidence value on an evaluation set. Having equalized each confidence value with the information actually conveyed, I can use the elementary sumrule to combine confidence values of different classifiers. Experiments for combined online/offline Japanese character recognition show clear improvements over the best single recognition rate. 1.
From Informational Confidence to Informational Intelligence
"... This paper is a continuation of my previous work on informational confidence. The main idea of this technique is to normalize confidence values from different sources in such a way that they match their informational content determined by their performance in an application domain. This reduces clas ..."
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This paper is a continuation of my previous work on informational confidence. The main idea of this technique is to normalize confidence values from different sources in such a way that they match their informational content determined by their performance in an application domain. This reduces classifier combination to a simple integration of information. The proposed method has shown good results in handwriting recognition and other applications involving classifier combination. In the present paper, I will focus more on the theoretical properties of my approach. I will show that informational confidence has the potential to serve as a theory for learning in general by showing that this approach naturally leads us to the famous Yin/Yang symbol of Chinese philosophy, a classic symbol describing two opposing forces. Furthermore, a closer inspection of the opposing forces and their interplay will reveal a new informationtheoretical meaning of the golden ratio, which describes the points where both confidence and counterconfidence merge into one force, with performance matching expectation. Although this is mainly a theoretical paper, I will present some practical results for handwritten Japanese character recognition.
DOCLIB: a software library for document processing
"... Most researchers would agree that research in the field of document processing can benefit tremendously from a common software library through which institutions are able to develop and share researchrelated software and applications across academic, business, and government domains. However, despi ..."
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Most researchers would agree that research in the field of document processing can benefit tremendously from a common software library through which institutions are able to develop and share researchrelated software and applications across academic, business, and government domains. However, despite several attempts in the past, the research community still lacks a widelyaccepted standard software library for document processing. This paper describes a new library called DOCLIB, which tries to overcome the drawbacks of earlier approaches. Many of DOCLIB’s features are unique either in themselves or in their combination with others, e.g. the factory concept for support of different image types, the juxtaposition of image data and metadata, or the addon mechanism. We cherish the hope that DOCLIB serves the needs of researchers better than previous approaches and will readily be accepted by a larger group of scientists.
ENTROPY, PERCEPTION, AND RELATIVITY
, 2006
"... In this paper, I expand Shannon’s definition of entropy into a new form of entropy that allows integration of information from different random events. Shannon’s notion of entropy is a special case of my more general definition of entropy. I define probability using a socalled performance function, ..."
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In this paper, I expand Shannon’s definition of entropy into a new form of entropy that allows integration of information from different random events. Shannon’s notion of entropy is a special case of my more general definition of entropy. I define probability using a socalled performance function, which is de facto an exponential distribution. Assuming that my general notion of entropy reflects the true uncertainty about a probabilistic event, I understand that our perceived uncertainty differs. I claim that our perception is the result of two opposing forces similar to the two famous antagonists in Chinese philosophy: Yin and Yang. Based on this idea, I show that our perceived uncertainty matches the true uncertainty in points determined by the golden ratio. I demonstrate that the wellknown sigmoid function, which we typically employ in artificial neural networks as a nonlinear threshold function, describes the actual performance. Furthermore, I provide a motivation for the time dilation in Einstein’s Special Relativity, basically claiming that although time dilation conforms with our perception, it does not correspond to reality. At the end of the paper, I show how to apply this theoretical framework to practical applications. I present recognition rates for a pattern recognition problem, and also propose a network architecture that can take advantage of general entropy to solve complex decision problems.
Combining Classifiers with Informational Confidence
"... Summary. We propose a new statistical method for learning normalized confidence values in multiple classifier systems. Our main idea is to adjust confidence values so that their nominal values equal the information actually conveyed. In order to do so, we assume that information depends on the actua ..."
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Summary. We propose a new statistical method for learning normalized confidence values in multiple classifier systems. Our main idea is to adjust confidence values so that their nominal values equal the information actually conveyed. In order to do so, we assume that information depends on the actual performance of each confidence value on an evaluation set. As information measure, we use Shannon’s wellknown logarithmic notion of information. With the confidence values matching their informational content, the classifier combination scheme reduces to the simple sumrule, theoretically justifying this elementary combination scheme. In experimental evaluations for script identification, and both handwritten and printed character recognition, we achieve a consistent improvement on the best single recognition rate. We cherish the hope that our informationtheoretical framework helps fill the theoretical gap we still experience in classifier combination, putting the excellent practical performance of multiple classifier systems on a more solid basis. 1