In proposed directional feature with energy method for offline signature verification system author simply merge the above two methods altogether to observe the effect of the merging. Energy, aspect ration and directional feature of a thinned and binarized signature image is calculated and fed as an input to a feed forward back propagation neural network for training and classification again this preprocessing is done in the same manner. Fig. 3 shows the effect of some of the preprocessing steps. (a) shows the original digitized signature image under consideration. Then from (b) to (e) result of binarization, denoising, thining and skew removal is shown respectively. (f) describes the result of segmentation process for which 100 segments is made for each signature and further processing is done with each segments found after this step. After complete preprocessing both the energy density and directional feature is computed and fed to an artificially intelligent network for training and classification.

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(b) Binarization
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(f) Segmentation : 100 segments done for each signature