Text area detection in digital documents images using textural features

Abstract

In this paper we propose a new texture-based method for extraction of text areas in a complex document image. Gabor filter, motivated by the multi-channel filtering approach of Human Visual System (HVS), has been employed to create energy map of the document. In this energy map we assumed that text areas were rich in high frequency components. Connected components (probable text characters) were extracted by binarization of the energy map with Otsu's adaptive threshold method. First non-text components such as pictures, lines, frames etc. were eliminated by Gabor filtering. As a novel approach, remaining non-text components were then eliminated by using character component interval tracing. Elimination that formed in two stage, enhanced the success of detecting text area on different kinds of digital documents.