A Hybrid Approach towards Cost Effective Model for Handwritten Character Recognition

Abstract

Handwritten character is gaining a lot of attention in the area of pattern recognition as its applications in various fields are increasing day by day. HCR system is providing us with a key factor to a paperless environment. Feature Extraction is a key part for a cost effective model for handwritten character recognition. Effective features improve the recognition rate and misclassification. A hybrid model provides better performance in comparison of the individual. Convolution neural networks are viewed to be more efficient to optimize the recognition ability of HCR system.