Comparative Analysis of various Performance Functions for Training a Neural Network - Kumar, Shobhit; Mishra, Vikash Kumar; Singh, Sapna; Vimal, Neeraj (International Journal of Computer Sciences and Engineering, 2014)
Handwriting Recognition (or HWR) is the ability of a computer to receive and interpret comprehensible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "Offline" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Neural Network concept is the most efficient recognition tool which is dependent on sample learning. Mean square error function is the basic performance function which is most broadly used and affects the network directly. Various performance functions are being evaluated in this paper so as to get a conclusion as to which performance function would be effective for usage in the network so as to produce an efficient and effective system. The training of back propagation neural network is done with the application of Offline Handwritten Character Recognition using MATLAB simulator.