
## 2.1 Energy Density [17]

In this method, two features are used for training. Aspect ratio is used as a global feature and energy density is used as local feature. Aspect ratio is the ratio of Height (maximum vertical distance) to length 
(maximum horizontal distance) of the signature. We have calculated it after skew removal. Energy density is defined as the total energy present in each segment. We have done 100 segments of each signature and energy density is obtained by counting the total number of 1s in each segment (i.e. Total White Pixels). 

Thus, we have a feature vector of size 101X1 for energy density method as final database. This final database is fed to the neural network to perform the desired function i.e. training or classification.

## 2.2 Chain-Code [18]: As A Directional Feature

![0_image_0.png](0_image_0.png)

![0_image_1.png](0_image_1.png)

Chain-code is based on the direction of the connecting pixels. Each pixel is observed for next connected pixel and their direction changes mapped by giving a numerical value to every possible direction. There are generally 8 connectivity is taken into consideration as shown in the Fig. 1. But in this paper we have used 4 connectivity i.e. 0, 1, 2 & 3. As another 4 directions i.e. 4, 5, 6 & 7 are simply the negation of 0, 1, 2 & 3 directions. To obtain chain-code top left corner is considered as origin and scanning is done left to right, top to bottom (refer Fig. 2). Each pixel has observed separately and direction vector for that pixel is noted down. This process is carried out until the last pixel has scanned. Now, the frequency of occurrence in a particular direction is calculated for each segment and the histogram for each segment is used it to train the neural network. 

## 3 Implementation And Verification

Implementation of the proposed approach is basically divided in two parts i.e. Training and Classification. Training belongs to preparing and training of neural network for doing the classification work with an optimum accuracy. The proposed signature verification system takes an image of the signature as input and verifies whether the input image matches with the genuine training signature image available in the database or not. The system can be broadly categorized on the basis of method used for pre-processing and feature extraction from the image database and final input given to the neural network. 

Raw Signature Database is gathered from 10 people and 110 Genuine & 110 Forgeries from each individual is collected (i.e. 2200 Signature Samples) and digitized using scanner, 300 dpi resolution. The first step for pre-processing is Binarization. It is used to produce binary image i.e. to convert colored (if any) image in black & white (i.e. in 0 or 1) format. In this paper global threshold is used for this purpose. 

Noise is filtered out using median filter. Thinning is done after noise filtering. Morphological operation is applied (in MATLAB) to performs the desired thinning. Rotation of signature patterns by a non predictive angle is one of the major difficulties. In this paper simply the concept of trigonometry is used for skew removal. Fig. 4 shows the effect of different stages of pre-processing. The next step of pre-processing is to extract the signature only from the whole image, by removing the image of extra paper remained. After signature extraction again resizing is carried out, and then segmentation process is completed to extract the local features of the signature (i.e. energy density or directional feature of each segment according to the method under test). Author has used 100 segment of each signature sample for further processing. In energy density method author has also used aspect ratio as a global feature to improve the performance of the system. Aspect ratio is extracted just before the resizing and segmentation. 