Word Extraction in Text/Graphic Mixed Image Using 3-Dimensional Graph Model

Automatic Text location, character recognition and image understanding of a given paper document are main objectives of computer vision area. The rst stage for these problems is extracting text information and separating graphic symbol from texts. Previous text location algorithm could not extract the negative text(e.g., newspaper headline) which is a white colored text on some solid background color plane. Also they could extract only the horizontal or vertical text in a document, so the inclined text or text on a circular arc can not be located by the previous works. In this paper, we propose a new extracting method for these negative texts and real texts from a text/graphics mixed document image. Also we propose a new word grouping method when texts are intersected each other or placed on a circular arc or an inclined line segment with an arbitrary orientation. The basic strategy of our algorithm is based on the frequency analysis of the run-length encoded le of the image segment. Generally the number of runs in the run-length encoding for a text(character) is smaller than that of graphic symbol. And the average and variance of the number of runs in a run-length encoding gives a nice characterization of symbols and texts. After isolating each letter in a document le, we need to group the related letters a word. This procedure is a crucial work for an automatic document processing, since the unit of the nal output of the document processing should be a word or a statement. For this procedure, we propose 3 dimensional neighborhood graph for grouping words and statement from a set of isolated letters obtained from the rst letter isolating phase. This graph maps each letter in a document to a vertex in 3-dimensional space according to the size of that letter. Experimental results show that more than 97% of words were successfully extracted from the text/graphics mixed document including negative texts. This result shows the usefulness of our character isolating algorithm and our 3-dimensional graph mapping for the document with oriental characters.