Classification of binary document images into textual or nontextual data blocks using neural network models

Abstract

This paper describes a new method for the classification of binary document images as textual or nontextual data blocks using neural network models. Binary document images are first segmented into blocks by the constrained run-length algorithm (CRLA). The component-labeling procedure is used to label the resulting blocks. The features for each block, calculated from the coordinates of its extremities, are then fed into the input layer of a neural network for classification. Four neural networks were considered, and they include back propagation (BP), radial basis functions (RBF), probabilistic neural network (PNN), and Kohonen's self-organizing feature maps (SOFMs). The performance and behavior of these neural network models are analyzed and compared in terms of training times, memory requirements, and classification accuracy. The experiments carried out on a variety of medical journals show the feasibility of using the neural network approach for textual block classification and indicate that in terms of both accuracy and training time RBF should be preferred.