Title :
Transformation invariant SOM clustering in Document Image Analysis
Author :
Marinai, Simone ; Marino, Emanuele ; Soda, Giovanni
Author_Institution :
Univ. di Firenze, Florence
Abstract :
In this paper, we propose the combination of the self organizing map (SOM) and of the tangent distance for effective clustering in document image analysis. The proposed model (SOM_TD) is used for character and layout clustering, with applications to word retrieval and to page classification. By using the tangent distance it is possible to improve the SOM clustering so as to be more tolerant with respect to small local transformations of the input patterns.
Keywords :
document image processing; pattern clustering; self-organising feature maps; SOM_TD model; character clustering; document image analysis; layout clustering; page classification; selforganizing map; tangent distance; transformation invariant SOM clustering; word retrieval; Artificial neural networks; Clustering algorithms; Image analysis; Neurons; Pattern recognition; Prototypes; Self organizing feature maps; Supervised learning; Text analysis; Unsupervised learning;
Conference_Titel :
Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on
Conference_Location :
Modena
Print_ISBN :
978-0-7695-2877-9
DOI :
10.1109/ICIAP.2007.4362777
