A statistically based, highly accurate text-line segmentation method

This paper describes a text-line identification and segmentation technique that is probability based, where all probabilities are estimated from an extensive training set of various kind of measurements of distances between the terminal and non-terminal entities with which the algorithm works. The off-line probabilities estimated in the training then drive all decisions in the on-line segmentation algorithm. On the UW-III database of some 1600 scanned document image pages, having some 105020 text lines, the algorithm identifies and segments 104773 correctly, an accuracy of 99.76%