Circular Blurred Shape Model for Multiclass Symbol Recognition

—In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code desig...

• Type: Journal
• Year: 2011
• Where: TSMC
• Authors: Sergio Escalera, Alicia Fornés, Oriol Pujol, Josep Lladós, Petia Radeva
• Keywords: Multiclass Symbol, Prior Blurring Degree, Shape Model Descriptor, TSMC 2011