We present a method to recover the dynamic writing order from static images of handwriting. The static handwriting is initially represented by its skeleton, which is then converted into a graph, whose arcs correspond to the skeleton branches, and nodes to either end point or branch point of the skeleton. Criteria derived by handwriting generation are then applied to transform the graph in such a way that all its nodes, but the first and the last, have an even degree, so that it can be traversed from the first to the last by using the Fleury's algorithm. The experimental results show that combining criteria derived from handwriting generation models with graph traversal leads to reconstruct the original sequence produced by a writer even in case of complex handwriting, i.e handwriting with retracing, crossings and pen-up's.

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.

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 writer's 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.

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.

The paper presents a Mixed Reality (MR) software system for assisting the operators during the assembly process of complex products. The proposed solution is composed of a Mixed Reality (MR) Graphical User Interface (GUI), developed on a holographic hardware device (Microsoft HoloLens 2). The software architecture of the MR tool and the data integration framework with a collaborative robot in the factory infrastructure are also presented. In this paper, the MR interface is also exploited for the safety of the operator in the workplace and for training an operator who is assembling complex products for the first time. The software system has been tested on the real case of a company operating in the automotive sector in which the operator carries out the assembly process of complex electromotive vehicles entirely manually. The experimental results, measured during a real assembly process cycle, show that the approach actively contributes to reduce the total assembly time by 30% and to achieve the reduction of the learning time of about 50%. Considering these preliminary results, the proposed solution can significantly improve the quality, the efficiency and enhance the safety of the operator during the whole assembly process.