An entropy based segmentation algorithm for computer-generated document images

This paper presents an efficient compression-oriented segmentation algorithm for computer-generated document images. In this algorithm, a document image is represented in a block-based multiscale pyramid. Then, image blocks will be characterized based on their entropy values of the intensity histogram, and the entropy distribution are assumed to be Gaussian priors in this work. We will discuss two methods, i.e., off-line and online training, to estimate model parameters. We use the multiscale Bayesian estimation to refine the classification results and generate the final segmentation result, where image blocks are classified into four classes, i.e., background, text, graphic and picture. It is expected that the proposed entropy-based segmentation will be suitable for compound document compression and two training approaches apply to different applications.