Comparing Handwritten Character Recognition by AdaBoostClassifier and KNeighborsClassifier

Abstract

Recognition of handwritten characters is a very tedious and challenging task. Handwritten Character Recognition can be considered as a sub-field of Optical Character Recognition. HCR or Handwritten Character Recognition includes Pattern Matching, Template Matching, etc. Handwritten Character Recognition finds it's uses in many fields, ranging from a smart bot to machine vision applications. It is used in each and every part of the newly evolving technology. HCR provides an easy way to reduce human effort and be more accurate in the decisions. It also shuts down the barriers of language communication between different person. They help in easy communication and better use of various information around the globe. There are several ways to approach Handwritten Character Recognition. In this paper we are going to approach Handwritten Character Recognition using AdaBoostClassifier and KNeighborsClassifier and compare the results between the two classifiers.