Classification Techniques for Handwritten Character Recognition

Abstract

In this paper, we present various classification methods for printedoptical character identification (POCR) similarly handwritten optical character identification (HOCR). Observation response of various method illustrate that which scheme produce batter recognition trueness in printed optical character identification (POCR) similarly handwritten optical character identification(HOCR). This article illustrate analysis of previous paper, and also distinguish the most important once out of the diversity of superior existing classification and feature extraction techniques and we will standardize the techniques by their feature circumstances and dataset used by different authors. It bring us to the performance of the algorithms produced to the expected efficiency.Feature withdrawal supporting to examine the shape controlled in the outline. While a quantity of feature taking out and categorization techniques are accessible, other than the picking of an exceptionallysuperior; technique decides the high degree of recognition correctness. A batch of author present investigation in this field and designnovel techniques of extraction and categorization. The corepurpose of this proposed article is to re-examine these techniques, so that the group of these techniques can be comprehended.