Handwriting recognition systems based on hidden Markov models commonly use a vector quantizer to get the required symbol sequence. In order to get better recognition rates semi-continuous hidden Markov models have been applied. Those recognizers need a soft vector quantizer which superimposes a statistical distribution for symbol generation. In general, Gaussian distributions are applied. A disadvantage of this technique is the assumption of a specific distribution. No proof can be given whether this presupposition holds in practice. Therefore, the application of a method which employs no model of a distribution may achieve some improvements. The paper presents the employment of a polynomial classifier as a replacement of a Gaussian classifier in the handwriting recognition system. The replacement improves the recognition rate significantly, as the results show.

Semi-continuous Hidden Markov Models (SCHMM) with gaussian distributions are often used in continuous speech or handwriting recognition systems. Our paper compares gaussian and tree-structured polynomial classi ers which have been successfully used in pattern recognition since many years. In our system the binary classi er tree is generated by clustering HMM states using an entropy measure. For handwriting recognition, gaussians are clearly outperformed by polynomial classi cation. However, for speech recognition, polynomial classi cation currently performs slightly worse because some system parameters are not yet optimized.

The authors deal with the recognition of writing style (whether a data field is hand or machine printed) in the context of form reading applications. Due to the form reader's hardware restrictions, the approach had to be based only on the knowledge of the surrounding rectangles of the black connected components of the data field. Different statistical classifiers were developed which were adapted to different feature vectors calculated separately for each data field. The output of these classifiers was combined, allowing a much higher performance than each single classifier. The combination was carried out by another polynomial (statistical) classifier using the estimations, not decisions, of these classifiers as the new feature vector. The improvement by combination was significant. Meanwhile the approach has proven its practical viability while running successfully in commercially distributed form readers.>

Two different approaches are described to combine the results of different classifiers. The first approach is based on the Dempster/Shafer theory of evidence and the second one is a statistical approach with some assumptions on the input data. Both approaches were tested for user-dependent recognition of on-line handwritten characters.>

The authors present a conceptual framework for solving the task of document analysis, which, in essence, consists in the conversion of the document's pixel representation into an equivalent knowledge network representation holding the document's content and layout. Starting on the pixel level, the formation of elementary geometric objects on which layout analysis as well as the definition of character objects is based is described. Character recognition accomplishes the mapping from geometric object to character meaning in ASCII representation. On the next level of abstraction words are formed and verified by contextual processing. Modeled knowledge about complete documents and about how their constituents are related to the application forms the highest level of abstraction. The various problems arising at each stage are discussed. The dependencies between the different levels are exemplified and technical solutions put forward.>