Abstract
We report on an efficient intelligent character recognition tool for the automatic treatment of handwritten bank transfer forms. The classification is based on nearest-neighbor algorithms and a novel binary clustering technique for the generation of large prototype sets. We introduce a new confidence measure which can be used on a decision tree structure to combine lowest error rates with a very high recognition speed. Likelihood vectors allow context correction by database queries based on dynamic programming techniques as well as an easy integration of different classifier approaches in a multi-agent environment. In this paper, we present all components of the prototype system and give details on its realization and on possible parallel implementations on embedded systems.
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© 1996 Springer-Verlag Berlin Heidelberg
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Neschen, M., Nübel, F. (1996). COGNITUS — Fast and reliable recognition of handwritten forms based on vector quantisation. In: Liddell, H., Colbrook, A., Hertzberger, B., Sloot, P. (eds) High-Performance Computing and Networking. HPCN-Europe 1996. Lecture Notes in Computer Science, vol 1067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61142-8_567
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DOI: https://doi.org/10.1007/3-540-61142-8_567
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