A New Forged Handwriting Detection Method Based on Fourier Spectral Density and Variation

Use of handwriting words for person identification in contrast to biometric features is gaining importance in the field of forensic applications. As a result, forging handwriting is a part of crime applications and hence is challenging for the researchers. This paper presents a new work for detecting forged handwriting words because width and amplitude of spectral distributions have the ability to exhibit unique properties for forged handwriting words compared to blurred, noisy and normal handwriting words. The proposed method studies spectral density and variation of input handwriting images through clustering of high and low frequency coefficients. The extracted features, which are invariant to rotation and scaling, are passed to a neural network classifier for the classification for forged handwriting words from other types of handwriting words (like blurred, noisy and normal handwriting words). Experimental results on our own dataset, which consists of four handwriting word classes, and two benchmark datasets, namely, caption and scene text classification and forged IMEI number dataset, show that the proposed method outperforms the existing methods in terms of classification rate

Automatic signature verification system

Philosophiae Doctor - PhDIn this thesis, we explore dynamic signature verification systems. Unlike other signature models, we use genuine signatures in this project as they are more appropriate in real world applications. Signature verification systems are typical examples of biometric devices that use physical and behavioral characteristics to verify that a person really is who he or she claims to be. Other popular biometric examples include fingerprint scanners and hand geometry devices. Hand written signatures have been used for some time to endorse financial transactions and legal contracts although little or no verification of signatures is done. This sets it apart from the other biometrics as it is well accepted method of authentication. Until more recently, only hidden Markov models were used for model construction. Ongoing research on signature verification has revealed that more accurate results can be achieved by combining results of multiple models. We also proposed to use combinations of multiple single variate models instead of single multi variate models which are currently being adapted by many systems. Apart from these, the proposed system is an attractive way for making financial transactions more secure and authenticate electronic documents as it can be easily integrated into existing transaction procedures and electronic communication

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Review on the analysis of questioned documents

During the last years (2000-2014), many publications concerning the forensic analysis of questioned documents have been published, and new techniques and methodologies arenowadays employed to overcome forensic caseworks. This article reviews a comprehensive collection of the works focused on this issue, dating studies, the analysis of inks from pens andprinters, the analysis of paper, the analysis of other samples related to questioned documents and studies on intersecting lines. These sections highlight the most relevant analytical studies by a wide range of analytical techniques. Separation and spectrometric techniques are critically discussed and compared, emphasizing the advantages and disadvantages of each one. Finally, concluding remarks on the research published are included

Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Offline Signature Verification for Arabic Language

Biometrics relies on biological features (e.g. finger print, iris or the retina) or behavioral features (voice, signature). Those features can be used for identity verification for an individual. For this it became one of the most trusted and natural ways to identify a person and controlling access to the systems. Signature is a behavioral biometric. Signature is not unique like iris or finger print as it can be forged. Automatic signature verification is divided into two areas depending on the way of data capturing: offline and online signature verification. In offline signature verification, the signature is scanned from a document using a scanner to get the image of the signature. In online signature, a digitizing tablet is used to collect the movements during the signing. In this work we present a system for offline signature verification. In this system the user has to submit a number of signatures which are used to extract two types of features, statistical features and structural features. A vector obtained from each of them is used to train propagation neural net in the verification stage. A test signature is then taken from the user, to compare it with those the net had been trained with. A test experiment was carried out with two sets of data are collected. One set is used as a training set for the propagation neural net in its verification stage. This set with four signatures form each user is used for the training purpose. The second set consisting of one sample of signature for each of the 20 persons is used as a test set for the system. A negative identification test was carried out using a signature of one person to test others’ signatures. The system gave encouraging results

Designing a comprehensive system for analysis of handwriting biomechanics in relation to neuromotor control of handwriting

A comprehensive system for investigation of biomechanical and neuromuscular processes involved with producing handwriting and drawing was developed. The system included a pen-like grip measuring device that enabled the variations of finger grip force associated with writing and drawing to be measured while holding the pen in tripod grip. The pen was integrated with a digitiser tablet for recording x,ycoordinates and pressure of the nib and a motion analysis system for recording the limb and hand kinematics. It was observed that for line drawing in the y-direction of the tablet, finger forces were directly related to pen tip movement and finger forces were modulated in a repeatable and predictable fashion, while this was not the case for line drawing in the x-direction. This was evidence for longstanding assumptions. Wrist rotation was required for production of lines in the x-direction without excessive deviation. For writing tasks, it was observed that no two tasks performed by one subject share an identical writing process, not even when the writing results are (nearly) identical. The neuromuscular control apparatus is highly flexible and works in a coordinated fashion that allows production of nearly equal end-results by means of different mechanical and therefore neuromuscular processes. For spiral drawing, tremor that originates from the fingers, hand and arm was quantified with the transducer pen. Limb joint kinematics were displayed in three dimensions with colour coding of coordinate sample numbers. This method can reveal the origin of some forms of limb tremor. Pen grip force patterns during signature writing were found to be characteristic for subjects, which relate to their individual pen-hand interaction, resulting from fine control of distal joints. Variation between trials of the same subject was observed, revealing adaptations of the computational processes during writing. The potential for signature verification by means of finger force recording was explored.A comprehensive system for investigation of biomechanical and neuromuscular processes involved with producing handwriting and drawing was developed. The system included a pen-like grip measuring device that enabled the variations of finger grip force associated with writing and drawing to be measured while holding the pen in tripod grip. The pen was integrated with a digitiser tablet for recording x,ycoordinates and pressure of the nib and a motion analysis system for recording the limb and hand kinematics. It was observed that for line drawing in the y-direction of the tablet, finger forces were directly related to pen tip movement and finger forces were modulated in a repeatable and predictable fashion, while this was not the case for line drawing in the x-direction. This was evidence for longstanding assumptions. Wrist rotation was required for production of lines in the x-direction without excessive deviation. For writing tasks, it was observed that no two tasks performed by one subject share an identical writing process, not even when the writing results are (nearly) identical. The neuromuscular control apparatus is highly flexible and works in a coordinated fashion that allows production of nearly equal end-results by means of different mechanical and therefore neuromuscular processes. For spiral drawing, tremor that originates from the fingers, hand and arm was quantified with the transducer pen. Limb joint kinematics were displayed in three dimensions with colour coding of coordinate sample numbers. This method can reveal the origin of some forms of limb tremor. Pen grip force patterns during signature writing were found to be characteristic for subjects, which relate to their individual pen-hand interaction, resulting from fine control of distal joints. Variation between trials of the same subject was observed, revealing adaptations of the computational processes during writing. The potential for signature verification by means of finger force recording was explored