Zr. Chi et al., HANDWRITTEN DIGIT RECOGNITION USING COMBINED ID3-DERIVED FUZZY RULES AND MARKOV-CHAINS, Pattern recognition, 29(11), 1996, pp. 1821-1833
Citations number
22
Categorie Soggetti
Computer Sciences, Special Topics","Engineering, Eletrical & Electronic","Computer Science Artificial Intelligence
In this paper, we present an approach for handwritten digit recognitio
n based on combined ID3-derived fuzzy rules and Markov chains. Both te
chniques use statistical models on the structural representation of di
git images. Skeleton images are adopted to produce fuzzy rules based o
n the ID3 approach, and contour images are used for the Markov chain b
ased approach. Decision trees produced from the ID3 algorithm are conv
erted 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 t
he Markov chain is used to perform sequential analysis and to match wi
th models. The two classifiers are then combined to complement each ot
her 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 cha
racter recognition, such as the scaling problem and lack of machine le
arning ability. Experimental results on NIST Special Database 3 show t
hat the combined classifier has a significantly improved performance i
n terms of substitution versus rejection rates. After about 15% of dig
its that cannot be classified with high confidence by the combined cla
ssifier are re-classified by a nearest neighbor classifier using optim
ized prototypes, the overall classification rate can be as high as 98.
6% without rejection. Copyright (C) 1996 Pattern Recognition Society.