Off-line Signature Identification Research Articles

Offline Signature Identification Using Deep Learning and Euclidean Distance

Hand signature is one of human characteristic that human have since birth, which can be used as identity recognition. A high accuracy signature recognition is needed to identify the right owner of signature. This study present signature identification using a combination method between Deep Learning and Euclidean Distance. 3 different signature datasets are used in this study which consist of SigComp2009, SigComp2011, and private dataset. Signature images preprocessed using binary image conversion, Region of Interest, and thinning. Several testing scenarios is applied to measure proposed method robustness, such as usage of various Pretrained Deep Learning, dataset augmentation, and dataset split ratio modifier. The best accuracy achieved is 99.44% with high precision rate.

Offline Signature Identification and Verification Based on Capsule Representations

Abstract Offline signature is one of the frequently used biometric traits in daily life and yet skilled forgeries are posing a great challenge for offline signature verification. To differentiate forgeries, a variety of research has been conducted on hand-crafted feature extraction methods until now. However, these methods have recently been set aside for automatic feature extraction methods such as Convolutional Neural Networks (CNN). Although these CNN-based algorithms often achieve satisfying results, they require either many samples in training or pre-trained network weights. Recently, Capsule Network has been proposed to model with fewer data by using the advantage of convolutional layers for automatic feature extraction. Moreover, feature representations are obtained as vectors instead of scalar activation values in CNN to keep orientation information. Since signature samples per user are limited and feature orientations in signature samples are highly informative, this paper first aims to evaluate the capability of Capsule Network for signature identification tasks on three benchmark databases. Capsule Network achieves 97 96, 94 89, 95 and 91% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 and 32×32 resolutions, which are lower than usual, respectively. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Capsule Network achieves average 91, 86, and 89% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 resolutions, respectively. Through this evaluation, the capability of Capsule Network is shown for offline verification and identification tasks.

Offline Signature Verification and Identification using Dimensionality Reduction

Offline Signature Verification and Identification using Dimensionality Reduction

Non-Western Script Based Off-Line Handwritten Signature Technology: A Survey

Recognition and verification systems plays very critical role in the area of information security as they are very essential to user certification. In resent years, off-line signature recognition and verification receiving renewed interest and only one of several techniques used to verify the identities of individuals, also that one of the biometric techniques. Signatures offer a secure means for confirmation and authorization in legal documents. Thus, nowadays the signature identification and verification becomes an indispensable part for including embedded signatures of automating the rapid processing of documents. Researchers have been proposed various approaches for handwritten signature recognition and verification in the past years. This paper presents a survey for non-western handwritten signature based offline signature verification and identification. In this area, the accuracy rates obtained so far from the available systems is not sufficiently high, and more researches on off-line signature verification as well as off-line signature identification are required.

Non-Western Script Based Off-Line Handwritten Signature Technology: A Survey

Recognition and verification systems plays very critical role in the area of information security as they are very essential to user certification. In resent years, off-line signature recognition and verification receiving renewed interest and only one of several techniques used to verify the identities of individuals, also that one of the biometric techniques. Signatures offer a secure means for confirmation and authorization in legal documents. Thus, nowadays the signature identification and verification becomes an indispensable part for including embedded signatures of automating the rapid processing of documents. Researchers have been proposed various approaches for handwritten signature recognition and verification in the past years. This paper presents a survey for non-western handwritten signature based offline signature verification and identification. In this area, the accuracy rates obtained so far from the available systems is not sufficiently high, and more researches on off-line signature verification as well as off-line signature identification are required.

Arabic Handwritten Signature Identification

This paper proposes a new intelligent off-line Arabic handwritten signature identification and verification system based on texture analysis. The system uses the texture as feature and back propagation neural network as classifier. The signature image is preprocessed by several operations (Noise removal, Conversion of the signature image to binary image, Finding outer rectangle, Thinning and Size normalization) then the fractal number and co-occurrence matrix are computed to estimate texture features. In this work, two off-line Arabic handwritten signature identification systems are constructed. The first one uses the nearest Euclidean distance, while the other uses back propagation neural network. The paper analyzes and compares the results obtained from the two proposed systems to show the robustness level of the proposed intelligence system. Furthermore, the proposed system was tested by using Genuine signatures and has achieved a CCR (Correct Classification Rate) of 100% in best cases, while it was tested by using Forged signatures it has achieved a CRR approximated to 96.3% in best cases. The experimental results showed that the proposed system is efficient and competent with other state-of-the-art texture-based off-line signature identification systems.

A Signature Identification Method Based on Strength and Strokes Direction

It was much more complex and difficult for off-line signature identification attributable to the limitation of available information. To solve the problem, a signature identification method based on strength and strokes direction was proposed. The signature image acquired was gray-scaled and filtered at the stage of preprocess; then the image was two-valued with different threshold based on strength feature, the regions which grayscale was less than threshold were retained; the strokes which possess distinctive directional feature were extracted by using mathematical morphology and combining different scales/directions structure element based on strokes direction feature; at last judgement was maked for sample in accordance with corresponding feature. Experimental results showed the proposed method can enhance accurate rate effectively, improve real-time performance, which was a try beneficial to apply new methods for off-line signature identification.

Offline signature verification and identification by hybrid features and Support Vector Machine

This paper emphasised an approach for offline signature verification and identification. Two image descriptors are studied, including Pyramid Histogram of Oriented Gradients (PHOG), and a direction feature proposed in the literature. Compared with many previously proposed signature feature extraction approaches, PHOG has advantages in the extraction of discriminative information from handwriting signature images. The significance of classification framework is stressed. With the benchmarking database “Grupo de Procesado Digital de Senales” (GPDS), satisfactory performances were obtained from several classifiers. Among the classifiers compared, SVM is clearly superior, giving a False Rejection Rate (FRR) of 2.5% and a False Acceptance Rate (FAR) 2% for skillful forgery, which compares sharply with the latest published results on the same dataset. This substantiates the superiority of the proposed method. The related issue offline signature recognition is also investigated based on the same approach., with an accuracy of 99% on the GPDS data from SVM classification.