Input Fuzzy Modeling for the Recognition of Handwritten Hindi Numerals

This paper presents the recognition of handwritten Hindi numerals based on the modified exponential membership function fitted to the fuzzy sets derived from normalized distance features obtained using the box approach. The exponential membership function is modified by two structural parameters that are estimated by optimizing the criterion function associated with the input fuzzy modeling. We then utilize a `reuse policy´ that provides guidance from past error values of the criteria function to accomplish the reinforcement learning. We also show how the `reuse policy´ improves the speed of convergence of the learning process over other strategies that learn without reuse. There is a 25-fold improvement in training with the use of the reinforcement learning. Experimentation is carried out on a limited database of around 3500 Hindi numeral samples. The overall recognition rate is found to be 95%