Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symbol Recognition by Error-Tolerant Subgraph Matching between Region Adjacency Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symbol Recognition: Current Advances and Perspectives
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
A new shape descriptor defined on the radon transform
Computer Vision and Image Understanding
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An incremental node embedding technique for error correcting output codes
Pattern Recognition
A Review of Shape Descriptors for Document Analysis
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Primitive segmentation in old handwritten music scores
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Attributed Programmed Graph Grammars and Their Application to Schematic Diagram Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A bag of notes approach to writer identification in old handwritten musical scores
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
From engineering diagrams to engineering models: Visual recognition and applications
Computer-Aided Design
Deforming the blurred shape model for shape description and recognition
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A keyword spotting approach using blurred shape model-based descriptors
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Minimal design of error-correcting output codes
Pattern Recognition Letters
A non-rigid appearance model for shape description and recognition
Pattern Recognition
A subspace approach to error correcting output codes
Pattern Recognition Letters
Writer identification in handwritten musical scores with bags of notes
Pattern Recognition
Notation-Invariant patch-based wall detector in architectural floor plans
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
On the design of an ECOC-Compliant Genetic Algorithm
Pattern Recognition
A new geometric descriptor for symbols with affine deformations
Pattern Recognition Letters
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Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance.