Titlebook: Machine Learning in Document Analysis and Recognition; Simone Marinai,Hiromichi Fujisawa Book 2008 Springer-Verlag Berlin Heidelberg 2008

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Decision-Based Specification and Comparison of Table Recognition Algorithms,on, and comparison of recognition algorithms. We propose an approach that centers on a process-oriented description. The approach is implemented using a new scripting language called RSL (Recognition Strategy Language), which captures the recognition decisions an algorithm makes as it executes. This

aesthetic

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Combining Classifiers with Informational Confidence,lues 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 well-known logarithmic notion of information. With the co

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Self-Organizing Maps for Clustering in Document Image Analysis,of artificial neural network that computes, during the learning, an unsupervised clustering of the input data arranging the cluster centers in a lattice. After an overview of the previous applications of unsupervised learning in document image analysis, we present our recent work in the field. We de

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Adaptive and Interactive Approaches to Document Analysis,orked applications. Context in document classification conventionally refers to language context, i.e., deterministic or statistical constraints on the sequence of letters in syllables or words, and on the sequence of words in phrases or sentences. We show how to exploit other types of statistical d

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Learning Matching Score Dependencies for Classifier Combination,es produced by different classifiers and the dependence between scores assigned to different classes by the same classifier. Whereas the possibility of first dependence is evident, and existing classifier combination algorithms usually account for this dependence, the second type of dependence is mo