Pixel Downsampling for Optimization of Artificial Neural Network for Handwriting Character Recognition

The aim of this study was to develop an image preprocessing model that utilize downsampling technique to reduce the pixel matrix to optimize artificial neural network in order to facilitate the handwriting recognition for letter A,B,C,D and E. In the proposed model, the handwriting images was first subjected to binarization process, the followed by the pixel matrix downsampling first using the column approach (C-DS), then combine raw and column approach (RC-DS). The compressed pixel (downsampled pixel matrix) then acted as an input vector for Artificial Neural Network (ANN). The functionality of the proposed method was demonstrated by its application to handwritten characters consisting of A, B, C, D and E examination choices. The results of the simulation indicated the proposed downsampling using combine column and row presented the higher accuracy (98.80%) and low pattern range (3.30%) with a minimum RMSE (0.1). The model further presented low execution time (560 Second) when compared to normal backpropagation. Thus base on the simulation results the proposed method outperformed the normal backpropagation and provide a reliable and efficient image preprocessing approach for the input of Artificial Neural Network.