TRANSLATION-, ROTATION- AND SCALE- INVARIANT RECOGNITION OF HAND-DRAWN SYMBOLS IN SCHEMATIC DIAGRAMS
Seong–Whan Lee, JIN H. KIM, Frans C. A. Groen · International Journal of Pattern Recognition and Artificial Intelligence · 1990
In this paper, a model-based scheme for recognizing hand-drawn symbols in schematic diagrams using attributed graph (AG) matching in the absence of any information concerning their pose (translation, rotation and scale) is described. The process of AG matching proceeds as follows. First, an observed AG (AG O ) is constructed from single-pixel-width line-representation of an observed symbol. Second, the pose of the AG O is estimated in terms of translation, rotation and scale with respect to the model AGs (AG M s ). The search space is effectively pruned by introducing the concept of control vertex and applying geometrical constraints in an early stage. In this step, a small number of candidate AG M s are selected. Third, correspondences between components of the observed AG after normalization (AG O N ) and those of the AG M s are found for the given poses. Fourth, distance measures between the AG O N and the AG M s are calculated, based upon the correspondences. Finally, the AG O N is classified as the AG M with the minimum distance. Experimental results for hand-drawn symbols with and without templates show that using AG matching is very efficient and successful for translation-, rotation- and scale-invariant recognition of hand-drawn symbols in schematic diagrams.