Distributed Autonomous Agents for Chines Document Images Segmentation

In Chinese document image processing, text and/or graphical block detection serves as an essential step in document layout analysis that in turn permits the eeective reasoning about the logical relationships among various text paragraphs and graphical entities for the purpose of document understanding. This paper presents a novel computational paradigm for extracting text/graphic blocks from Chinese document images, which is based on a notion of distributed autonomous agents. The primary features of the agents lie in that they are (1) adaptive to the locality of given images and hence eecient in locating the homogeneous image blocks, (2) reliable in performing image processing as the computation proceeds simultaneously from diierent image locations, (3) less sensitive to the noise in the given images as the computation disperses gracefully when it is moving away from the homogeneous blocks, and (4) easy to represent in their behaviors and evolvable in their performance. The paper, rst of all, describes the formalisms as well as behavioral characteristics of the agents, which is followed by a demonstration of the agents in detecting document blocks from some real-life images.