Multiresolution Morphological Approach to Document Image Analysis

An image-based approach to document image analysis is presented. The methods are motivated by a merged view of shape and textural image properties at multiple scales. The principal binary image operations are morphological and multiresolution. The generalized opening is introduced for extraction of both shape and texture from an image. Threshold reduction operations are introduced for performing efficient and controllable shape and texture transformations between resolution levels. Some problems, such as halftone or dark area segmentation, can be in large part solved by a sequence of threshold reductions. Aspects of the approach are illustrated by the problem of identifying italic and bold words in text, using word-level extraction at lowered resolution. The computational costs of the basic operations are given, so that algorithm efficienc y can be estimated, and the importance of operating at the lowest feasable resolution is demonstrated. For example, word segmentation and halftone extraction proceed in excess of 1.5x10 image pixels/second on a Sun Sparcstation2 .