Encoding of modified X-Y trees for document classification

Describes a method for classifying document images on the basis of their physical layout. The layout is described by means of a hierarchical description, the modified X-Y tree, that is derived from the classical X-Y tree segmentation algorithm taking into account cuts along lines in addition to cuts along white spaces between blocks. In order to reduce problems due to noise and the skew of the input image, the modified X-Y tree is built on top of regions extracted by a commercial OCR package. The tree is afterwards coded into a fixed-size representation that takes into account occurrences of tree patterns in the tree representing the page. Lastly, this feature vector is fed to an artificial neural network that is trained to classify document images. The system is applied to the classification of documents belonging to digital libraries. Examples of classes taken into account are "title page", "index" and "regular page". Many tests have been carried out on a data set of more than 600 pages from an online digital library. These tests allowed us to conclude that the use of modified X-Y trees is advantageous with respect to the classical X-Y decomposition for this classification task