Neural network based feature space generation for multiple databases of handwritten numerals

Describes the combination of multiple feature sets for multiple databases. We adapt MLPs as efficient feature set generators from the original input feature sets. The generated feature sets by MLPs are then coupled into new feature space that is quite useful for combination of multiple feature sets as well as accommodation of multiple partitioned databases. For our experiments four feature sets and three databases were used. Experimental results showed that the proposed feature set combination provides excellent classification performance and can be applied to general multiple feature spaces and database environments