Importance

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Abstract

Serial runlength data obtained by scanning an image includes 'slices' that each include the length and an ending pixel number of a successive string of connected black pixels. The runlength data is operated upon, line-by-line, by a decision tree classifier that creates a software 'object' including a first linked list of a plurality of further linked lists each of which contains all of the slices of a simple shape of the image. The slices of the object are entered into a 'frame' in the same order in which they are scanned and are 'filtered' to delete insignificant horizontal and vertical gaps between slices. The slopes of edges of the shapes are tested for linearity to determine which shapes can be represented as trapezoids that can be represented by four corner points and which must be represented as irregular blobs containing all of their slices. The resulting greatly compacted frame data representing the object is fed into a decision tree classifier that recognizes and assigns identifiers to divergences, convergences, and open ends of the object and creates a new linked list of linked lists representing the object in the form of blob records, trapezoid records, divergence records, and convergence records which then can be conveniently used in subsequent vectorization operations without the need to scan and recognize data representing the object.

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First Claim