Acta Informatica
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Regulated Rewriting in Formal Language Theory
Regulated Rewriting in Formal Language Theory
Character Recognition with k-Head Finite Array Automata
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A Parallel Thinning Algorithm with Two-Subiteration that Generates One-Pixel-Wide Skeletons
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Combining Structural and Statistical Features for the Recognition of Handwritten Characters
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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In this paper we introduce a new hybrid system for the automated recognition of hand-written characters - we combine the most promising approaches of the last decade, i.e., neural networks and structural/ syntactical analysis methods. The given patterns represent handwritten capital letters and digits stored in arrays. The first part of the hybrid system consists of the implementation of a neural network and yields a rapid and reliable pre-selection of the most probable characters the given pattern may represent. Depending on the quality and the special characteristics of the given pattern a flexible set of characters is communicated to the second part of the hybrid system, the structural analysis module. The final decision is based on the evaluation of the presence of features, being characteristic for a specific character, in the underlying pattern. Basically, the structural analysis module consists of graph controlled array grammar systems using prescribed teams of productions. We describe the main parts of the implemented hybrid system and demonstrate the power of our approach.