Pattern Recognition-Recognition of Handwritten Document Using Convolutional Neural Networks

Abstract

Handwritten documents recognition is a challenging task in the field of pattern recognition. It has an array of applications wherein recognition of words, alphabets, digits and other characters are the mandate. This review article mainly focuses review on Convolutional Neural Network (CNN) based handwritten documents recognition system. Basically, the handwritten recognition is divided into two different types: online and offline recognition. The difficulty of this system is dealing with huge variety of handwritten styles written by different writers. The system wants to recognize and identify such characters in effective manner. The scope of this review paper is to represent the merits and limitations of different techniques used in the development of recognition system. This paper definitely helps the researcher to get new idea to develop a new technique with good environment and architecture to propose more accuracy and less error rate. The major bottlenecks in this system are the issues of recognizing unconstrained handwritings like cursive, block, and tilt that cause huge variation in writing styles, the overlapping and the interconnections between characters. These systems are designed to assure high accuracy and reliability. This article summarizes different strategies in handwritten recognition system, which may help the researchers interested in this area to spot the research gap. The Future work of this paper is to implement a robust technique providing more accuracy and less error rate.