Majdoleen Sameer Abu-Taqa
Tawaddod Nabeel Sous
In the 21st century, it has become a trend that machines are replacing the man in many fields. But, there are a lot many fields still untouched to machines. But the evolution of modern computers and the development of the branch of Artificial Intelligence, gives man a chance to make a machine that can really replace man from the field which, up till are considered to be the fields reliable on human intellectual power. Signature is the characteristic of the particular person & hence used globally for identifying a person, validity of the documents signed, banking etc. Up till now, in banks where signature of a person is the basic code for transaction, the validity of the signature is generally checked by a man. This is a project, which simulates the ability of a man to recognize a signature from the standard signature he has. We have tried to implement a system which recognizes the signature. We deal with the signature as an image which is scanned through scanner. The image undergoes different normalization techniques and then we extract some features of it to be used as inputs to the fuzzy.
Signature of a person is an Important Biometric Attribute of a human being and is used for authorization purpose for decades. With a lot of computing power available with modern computers there is a vast scope to develop fast algorithms for signature recognition. There is a lot of research work is being conducted in this field. Various approaches are possible for signature recognition with a lot of scope of research. In this project we deal with an Off-line signature recognition technique, where the signature is capture and presented to the user in the format of image only. We use various image processing techniques to extract the parameters of signatures and verify the signature based on these parameters.
-The Signature Recognition System had passed through a series of steps as the following:
1-Select Signature image:
As the person signs his signature is scanned using a scanner and inserted into the system as an RGB image regardless to the pen color which is used in signing process.
The NOT of two images is carried out by performing the inversion operation on the corresponding pixels of the image to produce the output pixel value. The inversion technique can be used to get the negative of the image.
3-Gray Scale Image
Grayscale images are images without color, or achromatic images. The levels of a grayscale range from 0 (black) to 1 (white).
A binary image is a digital image that has only two possible values for each pixel.
*Apply Skeletonization Filter
Skeletonization is a process for reducing foreground regions in a binary image to a skeletal remnant that largely preserves the extent and connectivity of the original region while throwing away most of the original foreground pixels. To see how this works, imagine that the foreground regions in the input binary image are made of some uniform slow-burning material. Light fires simultaneously at all points along the boundary of this region and watch the fire move into the interior. At points where the fire traveling from two different boundaries meets itself, the fire will extinguish itself and the points at which this happens form the so called `quench line'. This line is the skeleton. Under this definition it is clear that thinning produces a sort of skeleton.
Another way to think about the skeleton is as the loci of centers of bi-tangent circles that fit entirely within the foreground region being considered. Figure 1 illustrates this for a rectangular shape.
Figure 1 Skeleton of a rectangle defined...