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We describe an implemented system which reads amounts on French checks images. This system is made of two modules which recognize independently the courtesy and the legal amount. The first one is based on a segmentation-by-recognition approach while the second uses hidden Markov models of words. The outputs of these two modules are combined into a system decision. A high reliability is achieved through this combination since the errors made by the recognition modules should be mostly uncorrelated.

This article deals with a new segmentation approach applied to unconstrained handwritten digits. The novelty of the proposed algorithm is based on the combination of two types of structural features in order to provide the best segmentation path between connected entities. This method was developed to be applied in a segmentation-based recognition system. In this article, we first present the features used to generate our basic segmentation points, and we define our segmentation paths depending on the encountered configurations with only few heuristic rules. Then, we present a strategy based on graphs in order to manager the segmentation. Finally, we evaluate the output of our segmenter using a integrated classifier with three different combination methods.