Expert system for handwritten numeral recognition using dynamic zoning

Abstract

This paper introduces an expert system for handwritten digit recognition. The system considers that a numeric handwritten character can be decomposed into vertical and horizontal strokes. Then, the positions where horizontal strokes are connected to the vertical strokes are extracted as features using dynamic zoning. These features are laid into a representative string which is validated by a regular expression following a matching pattern. The knowledge base is constructed from a decision tree structure that stores all well-formatted representative strings with the digits definitions. Finally, the inference engine tries to match unknown digits with the trained knowledge base in order to achieve the recognition. The promising results obtained by testing the system on the well-known MNIST handwritten database are compared with other approaches for corroborating its effectiveness.