Handwritten numeral recognition based on hierarchically self-organizing learning networks

Proposes a novel approach to tracing, representation, and subsequently recognition of handwritten numerals. The proposed approach extracts the geometrical and topological features of a numeral and, more importantly, provides the temporal (or dynamic) relationship among the strokes using a heuristic-rule-based tracing algorithm capable of generating a typical stroke sequence of a numeral. With the stroke sequence identified, one is able to extract the feature points (called critical points) of each stroke in an order given by the tracing sequence such that both static features, such as geometrical and topological features, and dynamical features, such as the temporal relationship among strokes, the number of strokes, and the direction of starting and ending strokes, can be preserved. Utilizing the temporal relationship among critical points and their corresponding X (or Y) coordinates as inputs and outputs, one can train a new neural network architecture using a supervised learning algorithm, referred to as a hierarchically self-organizing learning network, as a novel approach to handwritten numeral recognition