Combining multiple representations and classifiers for pen-based handwritten digit recognition

TLDR: This work investigates techniques to combine multiple representations of a handwritten digit to increase classification accuracy without significantly increasing system complexity or recognition time and implements and compares voting, mixture of experts, stacking and cascading.

Abstract: We investigate techniques to combine multiple representations of a handwritten digit to increase classification accuracy without significantly increasing system complexity or recognition time. We compare multiexpert and multistage combination techniques and discuss in detail in a comparative manner methods for combining multiple learners: voting, mixture of experts, stacking, boosting and cascading. In pen based handwritten character recognition, the input is the dynamic movement of the pentip over the pressure sensitive tablet. There is also the image formed as a result of this movement. On a real world database, we notice that the two multi layer perceptron (MLP) neural network based classifiers using these representations separately make errors on different patterns, implying that a suitable combination of the two would lead to higher accuracy. Thus we implement and compare voting, mixture of experts, stacking and cascading. Combined classifiers have an error percentage less than individual ones. The final combined system of two MLPs has less complexity and memory requirement than a single k nearest neighbor using one of the representations.