Offline shape recognition using flexible DCT grid

Extracting features out of binary shapes which are robust against scaling, noise, and rigid& elastic deformations is the purpose of this paper. the authors have utilized the amazing energycompaction property of discrete cosine transform, applied to variable size, non-overlapping blocksof input image, containing the shape to capture features (hereby called the signature of the shape).by keeping the number of these non-overlapping blocks the same, irrespective of the size of inputimage, is good against scaling, while applying various morphological operations on black and whiteversions of the shape and then picking a few lower frequency components makes the scheme workagainst noise and deformations. extracted signatures show amazing inter-class variation and intra-class similarity when classified using minimum distance and k-nn classifiers. the proposed system hasshown great accuracy on grec 2003 shape dataset (http://www.cvc.uab.es) [23], mpeg7 silhouette shapedatabase (http://www.imageprocessingplace.com/root_files_v3/image_databases.htm) [25] and toscatools shape dataset (http://tosca.cs.technion.ac.il/book/resources_data.html) [26].