Antonio Parziale

University of Salerno | UNISA · Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM)

The order in which the trajectory is executed is a powerful source of information for recognizers. However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images. Complex specimens can result in multiple pen-downs and in a high number of trajectory crossings yielding agglomerations of pi...

Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject's signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the si...

Roboticists and neuroscientists are interested in understanding and reproducing the neural and cognitive mechanisms behind the human ability to interact with unknown and changing environments as well as to learn and execute fine movements. In this paper, we review the system-level neurocomputational models of the human motor system, and we focus on...

As human-robot interaction rapidly spreads in numerous fields, the subject of robot acceptance gains increasing importance. Visual similarity to the human body, as occurs for humanoids, is generally not enough to guarantee acceptance in physical interaction, as acceptance directly links to comfort and ergonomics, which are measured in terms of the...

During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based model...

Reconstructing the trajectory from the static image of handwritten ink traces is useful in many practical applications envisaging handwriting analysis and recognition from offline data, as it allows the use of methods, algorithms, and tools that deal with online data, achieving better results than those achieved on offline data. In this work, the t...

The analysis of human movement provides important insights in several fields, such as biomechanics, neuroscience, psychology, medicine, and Artificial Intelligence (AI) [...]

A crucial aspect in human-robot collaboration is the robot acceptance by human co-workers. Based on previous experiences of interaction with their fellow beings, humans are able to recognize natural movements of their companions and associate them with the concepts of trust and acceptance. Throughout this process, the judgment is influenced by seve...

We review the number of contributions to the advancements in handwriting analysis for forensic applications that were presented at the biennial conferences of the International Graphonomics Society through its 20 editions. We introduce a taxonomy for the systematic analysis of the literature, propose a way to evaluate the overall interest and relev...

In signature verification, spatio-temporal features offer better performance than the ones extracted from static images. However, estimating spatio-temporal or spatial sequences in static images would be advantageous for recognizers. This paper studies recovered trajectories from skeleton-based images and their impact in automatic signature verific...

The state-of-the-art artificial intelligence tools for automatic diagnosis of Parkinson’s disease from handwriting require a lot of training samples from both healthy subjects and patients to exhibit impressive performance. Publicly available datasets include very few samples drawn by a small number of individuals and that limits the use of deep le...

Experimental studies led by Lashley and Raibert in the early phase of human movement science highlighted the phenomenon of motor equivalence, according to which complex movements are represented in the brain abstractly, in a way that is independent of the effector used for the execution of the movement. This abstract representation is known as moto...

The analysis of handwriting and drawing has been adopted since the early studies to help diagnose neurodegenerative diseases, such as Alzheimer’s and Parkinson’s. Departing from the current state-of-the-art methods that approach the problem of discriminating between healthy subjects and patients by using two- or multi-class classifiers, we propose...

Neurodegenerative diseases are caused by the progressive degeneration of nerve cells that affect motor skills and cognitive abilities with increasing severity. Unfortunately, there is no cure for this type of disease and their impact can only be slowed down with specific pharmacological and rehabilitative therapies. Early diagnosis, therefore, rema...

Background: Reconstructing the trajectory from the static image of handwritten ink traces is useful in many practical applications envisaging handwriting analysis and recognition from off-line data, as it allows to use methods, algorithms and tools that deal with on-line data, achieving better results than those achieved on off-line data. Methods:...

Nowadays, different automatic systems for writer identification and verification are available. On-line writer identification through automatic analysis of handwriting acquired with a tablet has been widely studied. Furthermore, the recent development of Commercial Off-The-Shelf (COTS) wearables with integrated inertial measurement units (IMUs) rec...

The order in which the trajectory is executed is a powerful source of information for recognizers. However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images. Complex specimens can result in multiple pen-downs and in a high number of trajectory crossings yielding agglomerations of pi...

In the last decades, early disease identification through non-invasive and automatic methodologies has gathered increasing interest from the scientific community. Among others, Parkinson's disease (PD) has received special attention in that it is a severe and progressive neuro-degenerative disease. As a consequence, early diagnosis would provide mo...

Digital libraries offer access to a large number of handwritten historical documents. These documents are available as raw images and therefore their content is not searchable. A fully manual transcription is time-consuming and expensive while a fully automatic transcription is cheaper but not comparable in terms of accuracy. The performance of aut...

The tradeoff between speed and accuracy of human movements has been exploited from many different perspectives, such as experimental psychology, workspace design, human–machine interface. This tradeoff is formalized by Fitts’ law, which states a linear relationship between the duration and the difficulty of the movement. The bigger is the required...

We address the problem of designing a machine learning tool for the automatic diagnosis of Parkinson’s disease that is capable of providing an explanation of its behavior in terms that are easy to understand by clinicians. For this purpose, we consider as machine learning tool the decision tree, because it provides the decision criteria in terms of...

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 thi...

Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject’s signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the si...

Lognormality has proven to be an effective way for handwriting modeling. It assumes that handwriting is a time superimposition of a sequence of commands issued by the central nervous system, each command producing a stroke, i.e. a movement with a lognormal velocity profile. Motor control theories, however, suggest that handwriting movements result...

This article propose a complete framework to recover the dynamic properties (i.e. velocity and pressure) of an on-line Western signature from an image-based signature. The framework is based on classical approaches to recover the writing order of the strokes and a novel process to recover the kinematic properties from thinned trajectories. In order...

Masquerade is a software for the analysis and the evaluation of morphological and dynamic features of handwriting. The software is tailored for handwriting experts, both from forensic and HR sectors, and is able to automatically measure handwriting features and reduce the time needed to produce a report.

Using an extracellular medium with high potassium/low magnesium concentration with the addition of 4-AP we induced epileptiform activity in combined hippocampus/entorhinal cortex slices of the rat brain [1]. In this in vitro model of temporal lobe epilepsy, we observed the repeating sequences of interictal discharge (IID) regimes and seizure-like e...

Handwritten signature is a biometric trait used for verifying a person’s identity. Automatic signature verification systems typically require a lot of specimens in order to model the signing habit of a subject but, in a real scenario, few signature samples are available. To overcome this problem, methods for creating human-like duplicated signature...

We present a method for writer identification that combines Forensic Handwriting Examination best practices with Pattern Recognition methodologies. The method is based upon a statistical characterization of the variability exhibited by a set of features that are meant to capture the distinctive aspects of document layout and handwriting. The featur...

We present the motivations and the results of an investigation aimed at establishing to which extent bumps observed in the ink trace do not correspond necessarily to hesitation of the writing movements but can be observed in fluent handwriting as well. We will show by experiments that this latter case occurs very often and provide an explanation in...

We propose a novel approach for helping content transcription of handwritten digital documents. The approach adopts a segmentation based keyword retrieval approach that follows query-by-string paradigm and exploits the user validation of the retrieved words to improve its performance during operation. Our approach starts with an initial training se...

ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment Ion channels are fundamental constituents determining the function of single neurons and neuronal circuits. To understand their complex interactions, the field of computational modeling has proven essential: since its emergence, thousands...

We suggest a model of signature verification based upon handwriting generation studies and derive from it the characterization of the signing habits of a subject. Such characterization is given in terms of the signature’s stability regions, which are obtained by exploiting shape and temporal information conveyed by the genuine signatures captured b...

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 o...

The large majority of methods proposed in literature for handwriting recognition assume that words are produced drawing large parts of the ink without lifting the pen, other than horizontal bars and dots. This fundamental assumption, however, does not always hold: while some educational systems provide explicit training for producing continuous han...

We present a model of the spinal cord in controlling one degree-of-freedom arm movements. The model includes both neural and musculoskeletal functions in an integrated framework. The model has been implemented by an artificial neural network coupled with a computational model of muscle publicly available. The experimental results show that the mode...

We propose an algorithm based on a model of visual perception that is meant to reflect the human judgment about the similarity of handwritten samples. The algorithm builds upon the Fuzzy Feature Contrast model and proposes an implementation of such a model in the domain of handwriting. The algorithm has been validated on the RIMES dataset, by compa...

Poster Presentation of a tool for supporting forensic document examiners.

A novel definition of stability regions and a new method for detecting them from on-line signatures is introduced in this paper. Building upon handwriting generation and motor control studies, the stability regions is defined as the longest similar sequences of strokes between a pair of genuine signatures. The stability regions are then used to sel...

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...

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 imp...

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 frequen...

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 habi...

We discuss the dynamics of signatures in the light of recent findings in motor learning, according to which a signature is a highly automated motor task and, as such, it is stored in the brain as both a trajectory plan and a motor plan. We then conjecture that such a stored representation does not necessarily include the entire signature, but can b...

We propose a new method for detecting the stability regions in on-line signatures. Building upon handwriting generation and motor control studies, the stability regions are defined as the longest common sequences of strokes between a pair of genuine signatures. The stability regions are then used to select the most stable signatures, as well as to...

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 dep...

The large majority of the methods proposed in literature for handwriting recognition assume that any word is produced without lifting the pen, other than horizontal bars and dots. This fundamental assumption, however, does not always hold: while some educational systems provide explicit training for producing continuous handwriting, minimizing the...

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 unkno...