Document image analysis using integrated image and neural processing

Abstract

In this paper we present robust algorithms for detecting the page orientation (portrait/landscape) and the degree of skew for binary document images, and a method for classification of binary document images into textual or non-textual data blocks using neural network models. The performance of four neural network models are compared in terms of training times, memory requirements, and classification accuracy, and it was found that the radial basis functions performed best. The experiments show the feasibility of building an integrated document analysis system for page orientation and skew angle detection, and textual block classification.