SPECIAL ISSUE on Document Analysis, 5(2):1-15, 2005. Combining Model-based and Discriminative Approaches in a Modular Two-stage Classification System: Application to Isolated Handwritten Digit Recognition Jonathan Milgram – Robert Sabourin – Mohamed Cheriet http://www.livia.etsmtl.ca

Motivations vs. Two categories of classification algorithms discriminative model-based Two types of problems ambiguous patterns outliers

Proposition Combining the two types of approaches in a two-stage classification system First stage: Model-based approach  Detect outliers  Classify unambiguous patterns Second stage: Discriminative approach  Refine decision  Reject ambiguous patterns

Overview Each class is modeled by a hyperplane and the projection distance is used as similarity measure In a first time the posterior probabilities are estimated by the first stage and only the highest are re-estimated by the second stage Support Vector Classifiers are trained using a pairwise coupling strategy

Experimental Validation Isolated Handwritten Digit Recognition A classical problem: A benchmark database: Modified NIST  28 x 28 grey-scale images  60,000 learning samples  10,000 test samples

  Some examples Ambiguous pattern Outlier Unambiguous pattern Projection on hyperplanes Class 1 2 3 4 5 6 7 8 9 10 dj(x) 5.07 6.47 5.51 4.91 5.25 5.30 1.88 6.27 6.02 6.05 5.08 6.77 5.51 5.91 5.28 5.69 5.63 5.45 6.03 4.93 5.37 4.87 4.80 3.24 5.15 5.45 3.50 4.78 3.47 0.6641 0.1537 0.1818 0.0001 0.0000 0.0000 1.0000 All the projection distances are high  the pattern can be rejected 0.0000 0.9967 0.0029 0.0001

Summary of the results Error rate at 0% of reject Reject rate at 0.1% of error Complexity (MFLOPs/pattern) First stage 4.09 % 28.59 % 0.4 Second stage 1.48 % 9.55 % 26.2 Combination 1.50 % 9.85 % 3.0

Conclusions Speeding up the decision making of SVC Making the outlier detection available Using a modular architecture suited to PR problems with large number of classes

Questions ?!? Correspondence to: milgram@livia.etsmtl.ca