Signatures are extensively used as a means of personal verification Manual signature-based authentication of a large number of documents is a very difficult and time consuming task Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable physical unique characteristics (hand geometry, iris scan, finger prints or DNA) or behavioural features Human signatures provide secure means for confirmation and authorization in legal documents So nowadays, automatic signature verification becomes an essential component In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of off-line signature verification systems

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Off-line signature verification systems: a survey

An offline signature verification system is proposed in this paper. The proposed model has two stages: preprocessing and eigen-signature construction. In the preprocessing stage, we convert a scanned signature to a shape form and eigen-signature construction is proposed for extracting the feature vector from a shape formed signature. Experiments have been conducted on the newly created Kannada offline signature database to exhibit the performance of the proposed model. A comparative analysis is provided with the recently proposed texture features based offline signature verification system on the publicly available gray signature database to exhibit the performance of the proposed model.

Eigen-signature: A robust and an efficient offline signature verification algorithm

Biometric systems are playing a key role in the multitude of applications and placed at the center of debate in the scientific research community. Among the numerous biometric systems, handwritten signature verification has got keen interest over the last three decades. Handwritten signature verification is the behavioral bio-metric system that discriminates the genuine signature from the pre-stored known signatures. It has been researched in the number of application areas like banking, financial and business transactions, cheque processing, access control and e-business etc. In this article, we surveyed the techniques of offline and online signature verification systems according to the taxonomy of classification model. A detailed background of signature verification system along with the available datasets are presented comprehensively. At the end, we presented the most notable challenges that guide the readers towards the current trends and future directions of the domain.

Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities

Some of the authors previously proposed a few nonlinear normalization methods using line density for handwritten characters and it was verified that those nonlinear methods were more effective than a linear method. Those nonlinear methods, however, could not use two-dimensional information of the line density, because the line density was projected onto the axes of coordinates. The paper proposes an extended nonlinear normalization method which uses two-dimensional information of the line density. It is confirmed that the proposed method is more powerful than conventional methods by experiments tested on the handwritten character database ETL-8.

Two-dimensional Extension of Nonlinear Normalization Method Using Line Density

In-air signature is a new modality which is essential for user authentication and access control in noncontact mode and has been actively studied in recent years. However, it has been treated as a conventional online signature, which is essentially a 2D spatial representation. Notably, this modality bears a lot more potential due to an important hidden depth feature. Existing methods for in-air signature verification neither capture this unique depth feature explicitly nor fully explore its potential in verification. Moreover, these methods are based on heuristic approaches for fingertip or hand palm center detection, which are not feasible in practice. Inspired by the great progress in deep-learning-based hand pose estimation, we propose a real-time in-air signature acquisition method which estimates hand joint positions in 3D using a single depth image. The predicted 3D position of fingertip is recorded for each frame. We present four different implementations of a verification module, which are based on the extracted depth and spatial features. An ablation study was performed to explore the impact of the depth feature in particular. For matching, we employed the most commonly used multidimensional dynamic time warping (MD-DTW) algorithm. We created a new database which contains 600 signatures recorded from 15 different subjects. Extensive evaluations were performed on our database. Our method, called 3DAirSig, achieved an equal error rate (EER) of 0 . 46 %. Experiments showed that depth itself is an important feature, which is sufficient for in-air signature verification. The dataset will be publicly available (https://goo.gl/yFdfdL).

3DAirSig: A Framework for Enabling In-Air Signatures Using a Multi-Modal Depth Sensor.

As the years passed by, computers became more powerful and automation became the need of generation. Humans tried to automate their work and replace themselves with machines. This effort of transition from manual to automatic gave rise to various research fields, and document character recognition is one such field. From the last few years, there is a sincere contribution from researchers for the development of optical character recognition systems for various scripts and languages. As a result of intensive research and development, there has been a significant improvement in handwritten devnagari text recognition. The main focus of this study is detailed survey of existing techniques for recognition of offline handwritten Hindi characters. It addresses all the aspects of Hindi character recognition starting from database to various phases of character recognition. The most relevant techniques of preprocessing, feature extraction and classification are discussed in various sections of this study. Moreover, this study is a zest of work accepted and published by research community in recent years. This study benefits its readers by discussing limitations of existing techniques and by providing beneficial directions of research in this field.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2017.0184

Handwritten Hindi character recognition: a review

In the era of growing technology, security is the major concern to avoid fakes and forgeries. There are various Biometric systems which help in personal identification, amongst those verification systems, one system is Signature Verification System. Signatures can be verified using online and offline systems. This paper explains the significance of offline systems and presents the survey of various approaches related to offline signature verification systems. Further, this paper provides the comparison of those approaches along with the features used, to help the researchers working in this field.

http://www.ijeit.com/vol%202/Issue%207/IJEIT1412201301_60.pdf

A Survey on Offline Signature Verification

Humans can easily recognize handwritten words, after gaining basic knowledge of languages. This knowledge needs to be transferred to computers for automatic character recognition. The work proposed in this paper tries to automate recognition of handwritten hindi isolated characters using multiple classifiers. For feature extraction, it uses histogram of oriented gradients as one feature and profile projection histogram as another feature. The performance of various classifiers has been evaluated using theses features experimentally and quadratic SVM has been found to produce better results.

Hindi handwritten character recognition using multiple classifiers

Character recognition is a well-known area because of its wide applications, including text-searchable documents, digital storage and support, and manual work automation. Recognizing characters is challenging, and, on top of that, writer’s writing style habits make handwritten character recognition even more challenging. This article addresses a set of features and classifiers that are less complex to implement and give significant recognition rates for Hindi language characters. The proposed work is based on two shape descriptors, such as (1) histogram of oriented gradients and (2) geometric moments. These descriptors, when used as features, reflect properties of characters that minimize intraclass variations and maximize interclass variations. The generated feature set is tested using two supervised classifiers, namely support vector machine (SVM) and multilayer perceptron (MLP). A thorough investigation into various evaluation parameters has been presented. Experimental results show that high recognition results are obtained when both shape descriptors are used in combination with an SVM classifier. This technique is evaluated on four publicly available databases, and it achieves a recognition rate of 96.8% on one of the datasets. A comparative analysis of the proposed method with relevant and recent works of this field proves its superiority. This work introduces a much less complex and promising approach toward isolated handwritten character recognition.

Hindi handwritten character recognition using oriented gradients and Hu-geometric moments

Character segmentation is an important step of Optical Character Recognition (OCR) system. The segmentation of touching symbols is one of the key factors which affect the performance of recognition system. Therefore, to make OCR systems more effective and accurate, segmentation of touching characters is an important task. This paper explains the concepts of touching characters and presents the survey of various approaches for touching character segmentation.

Madhuri Yadav

Papers

Handwritten Hindi character recognition: a review

A Survey on Offline Signature Verification

Hindi handwritten character recognition using multiple classifiers

Hindi handwritten character recognition using oriented gradients and Hu-geometric moments

A Survey on Touching Character Segmentation