Handwritten digit recognition using combined ID3-derived fuzzy rules and Markov chains

In this paper, we present an approach for handwritten digit recognition based on combined ID3-derived fuzzy rules and Markov chains. Both techniques use statistical models on the structural representation of digit images. Skeleton images are adopted to produce fuzzy rules based on the ID3 approach, and contour images are used for the Markov chain based approach. Decision trees produced from the ID3 algorithm are converted to a set of simplified rules which are then fuzzified into a set of fuzzy rules. To retain the classification performance, a two-layer perceptron is applied to optimize defuzzification parameters. On the other hand, a digit contour is traversed in a well-defined order and the Markov chain is used to perform sequential analysis and to match with models. The two classifiers are then combined to complement each other using a three-layer perceptron. The combined classifier achieves a good classification performance and at the same time it overcomes the difficulties in a conventional syntactic approach for handwritten character recognition, such as the scaling problem and lack of machine learning ability. Experimental results on NIST Special Database 3 show that the combined classifier has a significantly improved performance in terms of substitution versus rejection rates. After about 15% of digits that cannot be classified with high confidence by the combined classifier are re-classified by a nearest neighbor classifier using optimized prototypes, the overall classification rate can be as high as 98.6% without rejection.