Abstract: This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure.
Abstract: Biometrics, which refers to identifying an individual
based on his or her physiological or behavioral characteristics, has
the capability to reliably distinguish between an authorized person
and an imposter. Signature verification systems can be categorized as
offline (static) and online (dynamic). This paper presents a neural
network based recognition of offline handwritten signatures system
that is trained with low-resolution scanned signature images.
Abstract: Signature represents an individual characteristic of a
person which can be used for his / her validation. For such application
proper modeling is essential. Here we propose an offline signature
recognition and verification scheme which is based on extraction of
several features including one hybrid set from the input signature
and compare them with the already trained forms. Feature points
are classified using statistical parameters like mean and variance.
The scanned signature is normalized in slant using a very simple
algorithm with an intention to make the system robust which is
found to be very helpful. The slant correction is further aided by the
use of an Artificial Neural Network (ANN). The suggested scheme
discriminates between originals and forged signatures from simple
and random forgeries. The primary objective is to reduce the two
crucial parameters-False Acceptance Rate (FAR) and False Rejection
Rate (FRR) with lesser training time with an intension to make the
system dynamic using a cluster of ANNs forming a multiple classifier
system.
Abstract: In this work a new offline signature recognition system
based on Radon Transform, Fractal Dimension (FD) and Support Vector Machine (SVM) is presented. In the first step, projections of
original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained
vectors are calculated to construct a feature vector for each
signature. These vectors are then fed into SVM classifier for recognition of signatures. In order to evaluate the effectiveness of
the system several experiments are carried out. Offline signature
database from signature verification competition (SVC) 2004 is used
during all of the tests. Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition.
Abstract: There are two common methodologies to verify
signatures: the functional approach and the parametric approach. This
paper presents a new approach for dynamic handwritten signature
verification (HSV) using the Neural Network with verification by the
Conjugate Gradient Neural Network (NN). It is yet another avenue in
the approach to HSV that is found to produce excellent results when
compared with other methods of dynamic. Experimental results show
the system is insensitive to the order of base-classifiers and gets a
high verification ratio.