Handwritten Digit Recognition Using Soft Computing Tools
V. Susheela Devi, Musti Narasimha Murty · Studies in fuzziness and soft computing · 2000
This chapter deals with the handwritten digit recognition problem. We use a variety of classifiers for solving this problem. These classifiers include: nearest neighbour classifiers and fuzzy classifiers. A major contribution of this chapter is concerned with prototype selection for pattern classification. Genetic algorithms, simulated annealing, and tabu search are used for this purpose. The performance of various classifiers is compared based on experimental results obtained using a large data set of training and test patterns. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.