We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions—the sum rule—outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.

A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of classes so that they are comparable across different types of classifiers and different instances of a problem. The rankings can be combined by methods that either reduce or rerank a given set of classes. An intersection method and a union method are proposed for class set reduction. Three methods based on the highest rank, the Borda count, and logistic regression are proposed for class set reranking. These methods have been tested in applications on degraded machine-printed characters and words from large lexicons, resulting in substantial improvement in overall correctness.

This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters), or gray level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability, and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application. Feature extraction Optical character recogniti...

Whereas most state-of-the-art speech recognition systems use spectral or cepstral representations of the speech signal, there have also been some promising attempts at using articulatory information. These attempts have been motivated by two major assumptions: first, coarticulation can be modeled more naturally due to the inherently asynchronous nature of articulatory information. Second, it is assumed that the overall patterns in the speech signal caused by articulatory gestures are more robust to noise and speaker-dependent acoustic variation than spectral parameters. A third assumption can be made, viz. that acoustic and articulatory representations of speech can supply mutually complementary information to a speech recognizer, in which case the combination of these representations might be beneficial. Previously, articulatory-based speech recognizers have exclusively been developed for clean speech; the potential of an articulatory representation of the speech signal for noisy test...

We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian "ink generators" spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can ...

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 ...

Most distributed classification approaches view data distribution as a technical issue and combine local models aiming at a single global model. This however, is unsuitable for inherently distributed databases, which are often described by more than one classification models that might di#er conceptually. In this paper we present an approach for clustering distributed classifiers in order to discover groups of similar classifiers and thus similar databases with respect to a specific classification task. We also show that clustering distributed classifiers as a pre-processing step for classifier combination enhances the achieved predictive performance of the ensemble.

This paper presents Nonstationary Markovian Models and their application to recognition of strings of tokens. Domain specific knowledge is brought to bear on the application of recognizing ZIP Codes in US mailstream by use of postal directory files. These files provide a wealth of information on the delivery points (mailstops) corresponding to each ZIP Code. This data feeds into the models as n-grams statistics that are seamlessly integrated with recognition scores of digit images. A specially interesting facet of the model is its ability to excite and inhibit certain positions in the n-grams leading to the familiar area of Markov Random Fields. 1 The authors have previously described elsewhere [2] a methodology for deriving probability values from recognizer scores. These probability measures allow the Markov chain to be constructed in a truly Bayesian framework. We empirically illustrate the success of Markovian modeling in postprocessing applications of string recognition. We pres...

ABSTRACT: We address the problem of tracking efficiently feature points along image sequences. To estimate the undergoing movement we use an approach based on Kalman filtering which performs the prediction and correction of the features movement in every image frame. In this paper measured data is incorporated by optimizing the global correspondence set based on efficient approximations of the Mahalanobis distances (MD). We analyze the difference between using the MD and its efficient approximation in the tracking results, and also examine the related computational costs. Experimental results which validate our approach are presented. 1

A new representation method for recognition of handwritten charcters, called LLF (Local Line Fitting), is presented. The method, based on simple geometric operations, is very efficient and yields a relatively low-dimensional and distortion invariant representation. An important feature of the approach is that no preprocessing of the input image is required. A black