Adolfo Santoro - Academia.edu (original) (raw)
Papers by Adolfo Santoro
Abstract. The activity of the Natural Computation Laboratory at Department of Information and Ele... more Abstract. The activity of the Natural Computation Laboratory at Department of Information and Electrical Engineering focuses on modeling” evolutionary and brain processes”, bringing together diverse areas such as computer science, neuroscience, pattern recognition, evolutionary computation, information and optimization theory, and cross-pollinate computational models in different field such as handwriting analysis and recognition, robotics, video coding, pattern classification and machine learning. We report ...
2010 20th International Conference on Pattern Recognition, 2010
We present a method to recover the dynamic writing order from static images of handwriting. The s... more 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.
2010 20th International Conference on Pattern Recognition, 2010
We present a method to recover the dynamic writing order from static images of handwriting. The s... more 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.
2014 14th International Conference on Frontiers in Handwriting Recognition, 2014
We introduce a tool for quantitative evaluation of handwriting features largely adopted during fo... more 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.
Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on , Sep 18, 2012
We present a study for modeling handwriting styles that derives from handwriting generation studi... more 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.
Abstract. The activity of the Natural Computation Laboratory at Department of Information and Ele... more Abstract. The activity of the Natural Computation Laboratory at Department of Information and Electrical Engineering focuses on modeling” evolutionary and brain processes”, bringing together diverse areas such as computer science, neuroscience, pattern recognition, evolutionary computation, information and optimization theory, and cross-pollinate computational models in different field such as handwriting analysis and recognition, robotics, video coding, pattern classification and machine learning. We report ...
Image Analysis and Processing – ICIAP 2013, Sep 9, 2013
We present an experimental validation of a model of handwriting style that builds upon a neuro-co... more 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.
Abstract. The activity of the Natural Computation Laboratory at Department of Information and Ele... more Abstract. The activity of the Natural Computation Laboratory at Department of Information and Electrical Engineering focuses on modeling” evolutionary and brain processes”, bringing together diverse areas such as computer science, neuroscience, pattern recognition, evolutionary computation, information and optimization theory, and cross-pollinate computational models in different field such as handwriting analysis and recognition, robotics, video coding, pattern classification and machine learning. We report ...
2010 20th International Conference on Pattern Recognition, 2010
We present a method to recover the dynamic writing order from static images of handwriting. The s... more 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.
2010 20th International Conference on Pattern Recognition, 2010
We present a method to recover the dynamic writing order from static images of handwriting. The s... more 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.
2014 14th International Conference on Frontiers in Handwriting Recognition, 2014
We introduce a tool for quantitative evaluation of handwriting features largely adopted during fo... more 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.
Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on , Sep 18, 2012
We present a study for modeling handwriting styles that derives from handwriting generation studi... more 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.
Abstract. The activity of the Natural Computation Laboratory at Department of Information and Ele... more Abstract. The activity of the Natural Computation Laboratory at Department of Information and Electrical Engineering focuses on modeling” evolutionary and brain processes”, bringing together diverse areas such as computer science, neuroscience, pattern recognition, evolutionary computation, information and optimization theory, and cross-pollinate computational models in different field such as handwriting analysis and recognition, robotics, video coding, pattern classification and machine learning. We report ...
Image Analysis and Processing – ICIAP 2013, Sep 9, 2013
We present an experimental validation of a model of handwriting style that builds upon a neuro-co... more 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.