An approach for printed document labeling

A document image contains texts and non-texts, it may be printed, handwritten, or hybrid of both. In this paper we deal with printed document where textual region is of printed characters, and non-texts are mainly photo images. Here we propose a model which performs labeling of different components of a printed document image, i.e. identification of heading, subheading, caption, article and photo. Our method consists of a preprocessing stage where fuzzy c-means clustering is used to segment the document image into printed (object) region and background. Then Hough transformation is used to find white-line dividers of object region and grid structure examination is used to extract the non-text portion. After that, we use horizontal histogram to find text lines and then we label different components. Our method gives promising results on printed document of different scripts.