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

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

Flexible Neural Network Architecture for Handwritten Signatures Recognition

This article illustrates modeling of flexible neural networks for handwritten signatures preprocessing. An input signature is interpolated to adjust inclination angle, than descriptor vector is composed. This information is preprocessed in proposed flexible neural network architecture, in which some neurons are becoming crucial for recognition and adapt to classification purposes. Experimental research results are compared in benchmark tests with classic approach to discuss efficiency of proposed solution

Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach

Offline Signature Verification (OSV) is a challenging pattern recognition task, especially when it is expected to generalize well on the skilled forgeries that are not available during the training. Its challenges also include small training sample and large intra-class variations. Considering the limitations, we suggest a novel transfer learning approach from Persian handwriting domain to multi-language OSV domain. We train two Residual CNNs on the source domain separately based on two different tasks of word classification and writer identification. Since identifying a person signature resembles identifying ones handwriting, it seems perfectly convenient to use handwriting for the feature learning phase. The learned representation on the more varied and plentiful handwriting dataset can compensate for the lack of training data in the original task, i.e. OSV, without sacrificing the generalizability. Our proposed OSV system includes two steps: learning representation and verification of the input signature. For the first step, the signature images are fed into the trained Residual CNNs. The output representations are then used to train SVMs for the verification. We test our OSV system on three different signature datasets, including MCYT (a Spanish signature dataset), UTSig (a Persian one) and GPDS-Synthetic (an artificial dataset). On UT-SIG, we achieved 9.80% Equal Error Rate (EER) which showed substantial improvement over the best EER in the literature, 17.45%. Our proposed method surpassed state-of-the-arts by 6% on GPDS-Synthetic, achieving 6.81%. On MCYT, EER of 3.98% was obtained which is comparable to the best previously reported results

Offline signature verification using writer-dependent ensembles and static classifier selection with handcraft features

Orientador: Eduardo TodtDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 17/02/2022Inclui referências: p. 85-94Área de concentração: Ciência da ComputaçãoResumo: Reconhecimento e identificação de assinaturas em documentos e manuscritos são tarefas desafiadoras que ao longo do tempo vêm sendo estudadas, em especial na questão de discernir assinaturas genuínas de falsificações. Com o recente avanço das tecnologias, principalmente no campo da computação, pesquisas nesta área têm se tornado cada vez mais frequentes, possibilitando o uso de novos métodos de análise das assinaturas, aumentando a precisão e a confiança na verificação delas. Ainda há muito o que se explorar em pesquisas desta área dentro da computação. Verificações de assinaturas consistem, de forma geral, em obter características acerca de um a assinatura e utilizá-las para discerni-la das demais. Estudos propondo variados tipos de métodos foram realizados nos últimos anos a fim de aprimorar os resultados obtidos por sistemas de verificação e identificação de assinaturas. Diferentes formas de extrair características têm sido exploradas, com o o uso de redes neurais artificiais voltadas especificam ente para verificação de assinaturas, como a ResNet e a SigNet, representando o estado-da-arte nesta área de pesquisa. Apesar disso, métodos mais simples de extração de características ainda são muito utilizados, como o histograma de gradientes orientados (HOG), o Local Binary Patterns (LBP) e Local Phase Quantization (LPQ) por exemplo, apresentando, em muitos casos, resultados similares ao estado-da-arte. Não apenas isso, mas diferentes formas de combinar informações de extratores de características e resultados de classificadores têm sido propostos, como é o caso dos seletores de características, métodos de comitê de máquinas e algoritmos de análise da qualidade das características. D esta form a, o trabalho realizado consiste em explorar diferentes métodos de extração de características com binados em um conjunto de classificadores, de maneira que cada conjunto seja construído de forma dependente do autor e seja especificam ente adaptado para reconhecer as melhores características para cada autor, aprendendo quais com binações de classificadores com determinado grupo de características melhor se adaptam para reconhecer suas assinaturas. O desempenho e a funcionalidade do sistema foram comparados com os principais trabalhos da área desenvolvidos nos últimos anos, tendo sido realizados testes com as databases CEDAR, M CYT e UTSig. A pesar de não superar o estado-da-arte, o sistema apresentou bom desempenho, podendo ser com parado com alguns outros trabalhos importantes na área. Além disso, o sistema mostrou a eficiência dos classificadores Support Vector M achine(SVM ) e votadores para a realização da meta-classificação, bem como o potencial de alguns extratores de características para a área de verificação de assinaturas, com o foi o caso do Compound Local Binary Pattern(CLBP).Abstract: Signature recognition and identification in documents and manuscripts are challenging tasks that have been studied over time, especially in the matter of discerning genuine signatures from forgeries. With the recent advancement of technologies, especially in the field of computing, research in this area has become increasingly frequent, enabling the use of new methods of analysis of signatures, increasing accuracy and confidence in their verification. There is still much to be explored in research in this area within computing. Signature verification generally consists in obtaining features about a signature and using them to distinguish it from others. Studies proposing different types o f methods have been carried out in recent years in order to improve the results obtained by signature verification and identification systems. Different ways of extracting features have been explored, such as the use of artificial neural networks specifically aimed at verifying signatures, like ResNet and SigNet, representing the state-of-the-art in this research area. Despite this, simpler methods of feature extraction are still widely used, such as the Histogram of Oriented Gradients (HOG), the Local Binary Patterns (LBP) and the Local Phase Quantization (LPQ) for example, presenting, in many cases, similar results to the state-of-the-art. Not only that, but different ways of combining information from feature extractors and results from classifiers have been proposed, such as feature selectors, machine committee methods and feature quality analysis algorithms. In this way, the developed work consists in exploring different methods of features extractors combined in an ensemble, so that each ensemble is built in a writer-dependent way and is specifically adapted to recognize the best features for each author, learning which combinations of classifiers with a certain group of characteristics is better adapted to recognize their signatures. The performance and functionality of the system were compared w ith the m ain works in the area developed in recent years, w ith tests having been carried out with the CEDAR, M CYT and UTSig databases. Despite not overcoming the state-of-the-art, the system presented good performance, being able to be compared with some other important works in the area. In addition, the system showed the efficiency of Support Vector Machine(SVM ) classifiers and voters to perform the meta-classification, as well as the potential of some feature extractors for the signature verification area, such as the Compound Local Binary Pattern(CLBP)

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Graph-Based Offline Signature Verification

Graphs provide a powerful representation formalism that offers great promise to benefit tasks like handwritten signature verification. While most state-of-the-art approaches to signature verification rely on fixed-size representations, graphs are flexible in size and allow modeling local features as well as the global structure of the handwriting. In this article, we present two recent graph-based approaches to offline signature verification: keypoint graphs with approximated graph edit distance and inkball models. We provide a comprehensive description of the methods, propose improvements both in terms of computational time and accuracy, and report experimental results for four benchmark datasets. The proposed methods achieve top results for several benchmarks, highlighting the potential of graph-based signature verification

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition