Segmentation of Document Images Using Higher Order Statistics

This work presents an efficient post-segmentation method for separating text from the background in document images. For this task, this paper proposes the use of textured patterns to represent text in documents, instead of the standard black. It is shown that, in poor quality documents, text segmentation is more efficient when the characters in the document are represented in a halftoned gray level prior to printing. This occurs because the halftoning process induces statistical characteristics that help the text to be distinguished from noise or background. A typical case are noisy printed and scanned documents. Experiments validate the analysis and the applicability of the segmentation method. An important application for the method is in the postal service, where letters have their addresses segmented for automatic sorting.