Retrieval by Layout Similarity of Documents Represented with MXY Trees
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Using tree-grammars for training set expansion in page classification
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document zone content classification and its performance evaluation
Pattern Recognition
The Diagonal Split: A Pre-segmentation Step for Page Layout Analysis and Classification
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
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Abstract: In this paper we describe 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 by 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 skew of the input image, the Modified X-Y tree is built on top of regions extracted by a commercial OCR. 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", "regular page". Many tests have been carried out on a data-set of more than 600 pages belonging to an on-line Digital Library. These tests allowed us to conclude that the use of MXY trees is advantageous with respect to the classical XY decomposition for this classification task.