Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical, Nonparametric Methodology for Document Degradation Model Validation
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
Automatic Learning and Recognition of Graphical Symbols in Engineering Drawings
Selected Papers from the First International Workshop on Graphics Recognition, Methods and Applications
A new shape descriptor defined on the radon transform
Computer Vision and Image Understanding
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This paper deals with a progressive learning method for symbols recognition which improves its own recognition rate when new symbols are recognized in graphics documents. We propose a discriminant analysis method which provides allocation rules from learning samples with known classes. However a discriminant analysis method is efficient only if learning samples and data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each observation to take into account the parasitic effects between the data and the learning samples. We propose also an adaptation to consider the user feedback.