OFFLINE HANDWRITTEN DIGITRECOGNITION USING NEURALNETWORK
Sumedha B. Hallale, Geeta Salunke · International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering · 2013
Optical character recognition is a typical field of application of automatic classification methods. In this paper, we have introduced a whole new idea of recognition of isolated handwritten digits which is known to be a difficult task and still lacks a satisfactory technical solution. The present paper proposes a novel approach for recognition of handwritten digits i.e. neural network classification. Back propagation neural network is one of the simplest methods for training multilayer neural networks. In this paper, we designed a back propagated neural network and trained it with a set of handwritten digits. The average success rates of recognition of all digits are 91.2%.