Abstract: Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification.
Abstract: This paper proposes a stroke extraction method for use in off-line signature verification. After giving a brief overview of the current ongoing researches an algorithm is introduced for detecting and following strokes in static images of signatures. Problems like the handling of junctions and variations in line width and line intensity are discussed in detail. Results are validated by both using an existing on-line signature database and by employing image registration methods.
Abstract: Automatic reading of handwritten cheque is a computationally
complex process and it plays an important role in financial
risk management. Machine vision and learning provide a viable
solution to this problem. Research effort has mostly been focused
on recognizing diverse pitches of cheques and demand drafts with an
identical outline. However most of these methods employ templatematching
to localize the pitches and such schemes could potentially
fail when applied to different types of outline maintained by the
bank. In this paper, the so-called outline problem is resolved by
a cheque information tree (CIT), which generalizes the localizing
method to extract active-region-of-entities. In addition, the weight
based density plot (WBDP) is performed to isolate text entities and
read complete pitches. Recognition is based on texture features using
neural classifiers. Legal amount is subsequently recognized by both
texture and perceptual features. A post-processing phase is invoked
to detect the incorrect readings by Type-2 grammar using the Turing
machine. The performance of the proposed system was evaluated
using cheque and demand drafts of 22 different banks. The test data
consists of a collection of 1540 leafs obtained from 10 different
account holders from each bank. Results show that this approach
can easily be deployed without significant design amendments.