Membership Functions for Zoning-Based Recognition of Handwritten Digits

This paper focuses the role of membership functions in zoning-based classification. In fact, the effectiveness of a zoning methods depends not only on the way in which the pattern image is partitioned by the zoning, but also on the criteria adopted to define the way in which a feature influences the diverse zones. For this purpose, an experimental investigation is presented, that focuses the most valuable way in which a features spreads its influence on the zones of the pattern image. The experimental tests have been carried out in the field of handwritten digit recognition, using the numeral digits of the CEDAR database. The result points out the membership function has a paramount relevance on the classification performance and demonstrate that the exponential model outperforms other membership functions.