Despite the success of many pattern recognition systems in constrained domains, problems that involve noisy input and many classes remain difficult. A promising direction is to use several classifiers simultaneously, such that they can complement each other in correctness. This thesis is concerned with decision combination in a multiple classifier system that is critical to its success. A multiple classifier system consists of a set of classifiers and a decision combination function. It is a preferred solution to a complex recognition problem because it allows simultaneous use of feature descriptors of many types, corresponding measures of similarity, and many classification procedures. It also allows dynamic selection, so that classifiers adapted to inputs of a particular type may be applied only when those inputs are encountered. Decisions by the classifiers are represented as rankings of the class set that are derivable from the results of feature matching. Rank scores contain more ...

A computational theory of the visual recognition of words of text is developed. The theory, based on previous studies of how people read, includes three stages: hypothesis generation, hypothesis testing, and global contextual analysis. Hypothesis generation uses gross visual features, such as those that could be extracted from the peripheral presentation of a word, to provide expectations about word identity. Hypothesis testing integrates the information determined by hypothesis generation with more detailed features that are extracted from the word image. Global contextual analysis provides syntactic and semantic information that influences hypothesis testing. Algorithmic realization of the computational theory also consists of three stages. Hypothesis generation is implemented by extracting simple features from an input word and using those features to find a set of dictionary words with those features in common. Hypothesis testing uses this set of words to drive further selective image analysis that matches the input to one of the members of this set. This is done with a tree of feature tests that can be executed in several different ways to recognize an input word. Global contextual analysis is implemented with a process that uses knowledge of typical word-class transitions to improve the performance of the hypothesis testing stage. This is executable in parallel with hypothesis testing. This methodology is in sharp contrast to conventional machine reading algorithms which usually segment a word into characters and recognize the individual characters. Thus, a word decision is arrived at as a composite of character decisions. The algorithm presented here avoids the segmentation stage and does not require an exhaustive analysis of each character and thus is a character recognition algorithm. Statistical projections show the viability of all three stages of the proposed approach. Experiments with images of text show that the methodology performs well in difficult situations, such as touching and overlapping characters.

viii 1 Introduction 1 1.1 Handwritten Character Recognition : : : : : : : : : : : : : : : : : 1 1.2 Related Work : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 1.2.1 Feature Extraction : : : : : : : : : : : : : : : : : : : : : : 4 1.2.2 Handwriting Recognition : : : : : : : : : : : : : : : : : : : 6 1.3 Proposed Approach : : : : : : : : : : : : : : : : : : : : : : : : : : 9 1.4 Thesis Organization : : : : : : : : : : : : : : : : : : : : : : : : : : 12 2 Recognition and Representation of Numeral Patterns 13 2.1 Recognition Based on Human Logical Understanding : : : : : : : 13 2.1.1 Local Geometric Shape Features : : : : : : : : : : : : : : : 14 2.1.2 Learning of Different Contributions Among Local Shape Features : : : : : : : : : : : : : : : : : : : : : : : : : : : : 17 2.1.3 Learning of New Variants by Feature Generation : : : : : 17 2.2 Invariance Based on Biological Visual System : : : : : : : : : : : 18 2.2.1 Log-Polar Transformation : : : : : : : : : : : : : : : : :...

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ARTICLE IN PRESS Statistical Methodology xx (xxxx) xxx–xxx www.elsevier.com/locate/stamet Image recognition via deformable templates Ester Yen a, ∗,A.F.M. Smith b