Extraction of text lines and text blocks on document images based on statistical modeling

In this article, we developed a Bayesian model to characterize text line and text block structures on document images using the text word bounding boxes. We posed the extraction problem as finding the text lines and text blocks that maximize the Bayesian probability of the text lines and text blocks given the text word bounding boxes. In particular, we derived the so-called probabilistic linear displacement model (PLDM) to model the text line structures from text word bounding boxes. We also developed an augmented PLDM model to characterize the text block structures from text line bounding boxes. By systematically gathering statistics from a large population of document images, we are able to validate our models through experiments and determine the proper model parameters. We designed and implemented an iterative algorithm that used these probabilistic models to extract the text lines and text blocks. The quantitative performances of the algorithm in terms of the rates of miss, false, correct, splitting, merging, and spurious detections of the text lines and text blocks are reported. o 1996 John Wiley & Sons, Inc.