PLA using RLSA and a neural network

Abstract

This paper describes a new method for document page layout analysis. The proposed approach is based on the use of the run-length smoothing algorithm (RLSA) and a neural network block classified (NNBC). The RLSA is used locally and globally for the block segmentation by using optimal pre-estimated smoothing values. The NNBC is used in the classification steps of the method as a tool which classifies the blocks of the document into basic classes or subclasses. The NNBC consists of a principal component analyzer (PCA) and a self-organized feature map (SOFM). The input feature vector is a set of features corresponding to the contents and the relationships of 3×3 masks. This set is selected by using a statistical selection procedure, and provides textural information. In the final step, and after the application of a grouping procedure, the document blocks are classified as text frames and isolated text lines, graphics and halftones, or into secondary subclasses corresponding to special cases of the basic classes. The proposed method can identify blocks that cannot be separated with horizontal and vertical cuts, and gives very correct classification even on documents of bad scanning quality. The performance of the method has been extensively tested on a variety of documents. Several examples illustrate the strength and the effectiveness of the methodology.