Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods

Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201

Use of colour for hand-filled form analysis and recognition

Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system

Offline Signature Verification Scheme

Offline signature verification schemes are necessary to determine the authenticity and genuineness of a variety of things which require certification using signatures. Most offline verification schemes till date have required perfect alignment of the signature to the specified axes. However there are situations when the sample to be verified may not be aligned to the required axis. In that situation the current verification schemes could reject the signature even though it may be genuine. The suggested scheme aims to make the verification of signatures size and angle invariant. The invariance can be achieved by scaling and rotational manipulations on the target image. The shape of a person’s signature remains similar in all translational, scaled and rotational alignments of the sign. That is the number of crests, toughs and curves remains the same irrespective of the size and orientation of the image. The ratio between consecutive crests and troughs there by remain the same and hence can be used to determine the genuineness of a signature. The proposed scheme also proposes a novel way to store the information extracted from the image after processing. The ratios obtained for verification can be stored in a linear array, which required much less space as compared to the previously followed schemes. The success of the proposed scheme can be determined from the appreciable FARs and FAAs

Offline signature verification scheme using feature extraction method

In this project a new improved offline signature verification scheme has been proposed. The scheme is based on selecting 60 feature points from the geometric centre of the signature and compares them with the already trained feature points. The classification of the feature points utilizes statistical parameters like mean and variance. The suggested scheme discriminates between two types of originals and forged signatures. The method takes care of skill, simple and random forgeries. The objective of the work is to reduce the two vital parameters False Acceptance Rate (FAR) and False Rejection Rate (FRR) normally used in any signature verification scheme. Comparative analysis has been made with standard existing schemes. The Algorithms are based on the Geometric Center of an image so images are splitted into different parts to get the geometric centers of each which are called as Feature points in our thesis. We have taken 60(30+30) Feature points for calculation purpose(in extended Algorithm). As Feature points increases results will be more accurate but complexity and time require for testing will be more. So we have taken 60 feature points which improves security and maintains same complexity level. All calculations are done on the basis of these feature points. Results are expressed in terms of FAR (False Acceptance Rate) and FRR (False Rejection Rate) and subsequently compare these results with other existing Techniques. Results obtained by this algorithm are quite impressive. Random and Simple forgeries are eliminated and skilled forgeries are also eliminated in greater extent. As signature image is tested rigorously so FRR is more in the Algorithm proposed by us

TEXT CONTENT DEPENDENT WRITER IDENTIFICATION

Text content based personal Identification system is vital in resolving problem of identifying unknown document’s writer using a set of handwritten samples from alleged known writers. Text written on paper document is usually captured as image by scanner or camera for computer processing. The most challenging problem encounter in text image processing is extraction of robust feature vector from a set of inconstant handwritten text images obtained from the same writer at different time. In this work new feature extraction method is engaged to produce active text features for developing an effective personal identification system. The feature formed feature vector which is fed as input data into classification algorithm based on Support Vector Machine (SVM). Experiment was conducted to identify writers of query handwritten texts. Result show satisfactory performance of the proposed system, it was able to identify writers of query handwritten texts

Signature Verification Approach using Fusion of Hybrid Texture Features

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio

Mapping of cell nuclei based on contour warping

The dynamics of genome regions are associated to the functional or dysfunctional behaviour of the human cell. In order to study these dynamics it is necessary to remove all perturbations coming from movement and deformation of the nucleus, i.e. the container holding the genome. In literature models have been proposed to cope with the transformations corresponding to nuclear dynamics of healthy cells. However for pathological cells, the nucleus deforms in an apparently random way, making the use of such models a non trivial task. In this paper we propose a mapping of the cell nucleus which is based on the matching of the nuclear contours. The proposed method does not put constraints on the possible shapes nor on the possible deformations, making this method suited for the analysis of pathological nuclei

Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour

Authentication is very much essential in managing security. In modern times, it is one in all priorities. With the advent of technology, dialogue with machines becomes automatic. As a result, the need for authentication for a variety of security purposes is rapidly increasing. For this reason, biometrics-based certification is gaining dramatic momentum. The proposed method describes an off-line Genuine/ Forgery signature classification system using radon transform and K-Nearest Neighbour classifier. Every signature features are extracted by radon transform and they are aligned to get the statistic information of his signature. To align the two signatures, the algorithm used is Extreme Points Warping. Many forged and genuine signatures are selected in K-Nearest Neighbour classifier training. By aligning the test signature with each and every reference signatures of the user, verification of test signature is done. Then the signature can be found whether it is genuine or forgery. A K-Nearest Neighbour is used for classification for the different datasets. The result determines how the proposed procedure is exceeds the current state-of-the-art technology. Approximately, the proposed system’s performance is 90 % in signature verification system