Antonio Parziale

Exploiting Stability Regions for Online Signature Verification

We present a method for finding the stability regions within a set of genuine signatures and for selecting the most suitable one to be used for online signature verification. The definition of stability region builds upon motor learning and adaptation in handwriting generation, while their selection exploits both their ability to model signing habits and their effectiveness in capturing distinctive features. The stability regions represent the core of a signature verification system whose performance is evaluated on a standard benchmark.

Temporal evolution in synthetic handwriting

Method of synthesizing the temporal evolution of handwriting from childhood to adulthood.Synthesis of both online and offline handwriting.Parameters (E, D,t,K) for dealing with synthesized handwriting evolution.Method for comparing temporal evolution of real and synthetic handwriting. New methods for generating synthetic handwriting images for biometric applications have recently been developed. The temporal evolution of handwriting from childhood to adulthood is usually left unexplored in these works. This paper proposes a novel methodology for including temporal evolution in a handwriting synthesizer by means of simplifying the text trajectory plan and handwriting dynamics. This is achieved through a tailored version of the kinematic theory of rapid human movements and the neuromotor inspired handwriting synthesizer. The realism of the proposed method has been evaluated by comparing the temporal evolution of real and synthetic samples both quantitatively and subjectively. The quantitative test is based on a visual perception algorithm that compares the letter variability and the number of strokes in the real and synthetic handwriting produced at different ages. In the subjective test, 30 people are asked to evaluate the perceived realism of the evolution of the synthetic handwriting.

Modeling Handwriting Style: A Preliminary Investigation

We present a study for modeling handwriting styles that derives from handwriting generation studies, according to which handwriting is a temporal sequence of elementary movements. Hence, handwriting style results from the way those movements are actually performed and sequentially executed to reach fluency. We conjecture that handwriting styles depend on two main factors: the shape of the traces corresponding to the elementary movements and the way these traces are connected. To prove this conjecture, and the handwriting style model we have derived from it, we have designed an experiment in which handwriting samples are described by only two parameters and then clustered. The experimental results show that, despite its simplicity, the proposed method is able to capture the distinctive aspects of handwriting styles behind the handwriting samples, even when the writers deliberately attempts to modify it, and therefore corroborate our conjecture.

Modelling Visual Appearance of Handwriting

We present an experimental validation of a model of handwriting style that builds upon a neuro-computational model of motor learning and execution. We hypothesize that handwriting style emerges from the concatenation of highly automated writing movements, called invariants, that have been learned by the subject in correspondence to the most frequent sequence of characters the subject is familiar with. We also assume that the actual shape of the ink trace contains enough information to characterize the handwriting style. The experimental results on a data set containing genuine, disguised, and forged (both skilled and naive) documents show that the model is an effective tool for modeling intra-writer and inter-writers variability and provides quantitative estimation of the difference between handwriting styles that is in accordance with the difference in the visual appearance of the handwriting.

Reading Cursive Handwriting

We present a method for off-line reading of cursive handwriting, which derives from modelling handwriting as a complex movement. The method includes a step for recovering the writing order from static images of handwriting, a segmentation algorithm that decomposes the “unfolded” ink into strokes, an ink matching step to compare the ink of the unknown handwriting with those of a set of reference words, of whom the transcripts are given, and a graph search algorithm to search for the best interpretation among the possible ones. The method does not involve any feature extraction, nor a classification stage and may benefit from a linguistic context, if available. We report the results of experiments on 8,000 samples, draw some conclusions and outline further developments.

An Interactive Tool for Forensic Handwriting Examination

We introduce a tool for quantitative evaluation of handwriting features largely adopted during forensic examination of questioned documents. The tool is based on a model of handwriting generation and execution according to which handwriting is composed of elementary movements, called strokes, whose order and timing of execution has been learned and stored in the brain. Thus, what characterizes handwriting individuality, and therefore should be inferred from the samples available, is the way the sequence of strokes are executed. The tool does not aim at reaching a conclusion on the writers identity when comparing two documents, but provides the quantitative evaluation of a set of features that can be used by the expert to support his/her conclusion. Although the tool is meant to proceed automatically from the scanned image of the document to the quantitative evaluation of the features, it is equipped with an interface that allows the expert to follow the automatic procedure step-by-step and even to modify the output of any step and to modify it in case it is deemed as incorrect. The tool automatically produces a customizable report to illustrate the procedure, the features computation and to show the computed features values in both numerical and graphical form.

Rejecting Both Segmentation and Classification Errors in Handwritten Form Processing

The form processing systems commercially available include a verification step during which a human operator verifies the output provided by the system to ensure 100% accuracy. In order to reduce the time and the cost of such a stage, the OCR engine incorporated into the system provides a reliability measure of the classification to be used for implementing a reject option: in this way only rejected samples are passed to the verification stage. Most of the strategies for designing such a reject option consider that the source of classification errors are within the OCR engine. Such an assumption becomes less reasonable as the forms become less structured, as in case when boxes are provided for the entire data field and not only for isolated characters. Under these circumstances, we investigate to which extent the reliability measure provided by an OCR engine designed to deal with boxed isolated characters can be used to detect both segmentation and classification errors. The experimental results, obtained on a large data set of forms currently in use by a large organization, show that the proposed method successfully achieves its aim. It represents a powerful tool for the system manager to plan system enhancement as the volume of forms containing less constrained data fields increases.

Quantitative evaluation of features for Forensic Handwriting Examination

We propose a quantitative approach to both feature evaluation and comparison that combines Forensic Handwriting Examination best practices with Pattern Recognition methodologies. The former provide a set of features that are meant to capture the distinctive aspects of handwriting, the latter the computational tools for the quantitative evaluation of the features values as well as for their comparison. We will show that such a combined approach leads to a procedure that is theoretically sounds and can be expressed in terms the document examiners are familiar with. Eventually, we will suggest possible ways of using the results of the proposed approach in forensic handwriting examiners casework.

Recovering Segmentation Errors in Handwriting Recognition Systems

Most handwriting recognition systems need a mechanism for handling classification errors. These errors are typically caused by the large shape variability of the handwriting produced by different writers and by the segmentation errors, which occur when the word recognition process is performed by extracting and classifying single characters. In this paper, in order to reduce the segmentation errors, we propose a hierarchical recognition system composed of two classification modules. The first one discriminates isolated characters from cursive fragments using specifically devised features. The second one is an OCR engine that receives as input only those samples classified as isolated characters in the previous module. The whole system works like a highly reliable OCR that rejects most of the cursive fragments avoiding their incorrect classification. The experimental results confirmed the effectiveness of the proposed system.

Writer Verification in Forensic Handwriting Examination: A Pilot Study

Writer verification in forensic handwriting examination is usually performed on just a few lines of text available in both genuine and questioned documents. In such a case, the only source of information being the feature values measured on the document at hand, the large majority of the methods proposed in the realm of pattern recognition cannot be applied, as the large set of samples they require for training is not available. In this study we investigate to which extent a statistical characterization of the variability exhibited by a set of features commonly adopted by forensic handwriting examiners may provide valuable clue to weight the evidence drawn from the available data. We argue that such a characterization should be given in terms of parameters that can be directly linked to the perceived variation between documents, so as to be easy to understand and to report in courts. The results of a set of experiments show that simple statistical parameters, such as mean and standard error of the mean, may provide an effective way to establish both the reliability of a feature for the case at hand, as well as its relevance for verification.