Direct extraction of topographic features for gray scale character recognition

Optical character recognition (OCR) traditionally applies to binary-valued imagery although text is always scanned and stored in gray scale. However, binarization of multivalued image may remove important topological information from characters and introduce noise to character background. In order to avoid this problem, it is indispensable to develop a method which can minimize the information loss due to binarization by extracting features directly from gray scale character images. In this paper, we propose a new method for the direct extraction of topographic features from gray scale character images. By comparing the proposed method with Wang and Pavlidis´ method, we realized that the proposed method enhanced the performance of topographic feature extraction by computing the directions of principal curvature efficiently and prevented the extraction of unnecessary features. We also show that the proposed method is very effective for gray scale skeletonization compared to Levi and Montanari´s method.