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In this paper, we present a pressure characterization and normalization procedure for online handwritten acquisition. Normalization process has been tested in biometric recognition experiments (identification and verification) using online signature database MCYT, which consists of the signatures from 330 users. The goal is to analyze the real mismatch scenarios where users are enrolled with one stylus and then, later on, they produce some testing samples using a different stylus model with different pressure response. Experimental results show: 1) a saturation behavior in pressure signal 2) different dynamic ranges in the different stylus studied 3) improved biometric recognition accuracy by means of pressure signal normalization as well as a performance degradation in mismatched conditions 4) interoperability between different stylus can be obtained by means of pressure normalization. Normalization produces an improvement in signature identification rates higher than 7% (absolute value) when compared with mismatched scenarios., Comment: 11 pages, published in IEEE Access, vol. 9, pp. 18443-18453, 2021

International audience; This paper presents a method for fine analysis of children handwriting on pen-based tablets. This work is in the context of the P2IA project, funded by the French government, which aims at designing a virtual notebook in order to foster handwriting learning for primary school pupils. In this work, we consider the task of analysing handwritten words in the context of a dictation exercise. This task is complex due to different factors: the children do not master yet the morphological aspects of handwriting, nor do they master orthography or translating phonetic sounds to actual graphemes (parts of word). In order to tackle this problem, we extend to the context of dictation exercises an analysis engine that was developed previously to deal with copying exercises. Two strategies were developed, the first one is a baseline approach and relies on double child input: the pupil types the word on a virtual keyboard after writing it with the stylus, thus the prior knowledge of the written word will drive the engine analysis. The second one relies on a single input: the child handwritten strokes. To drive the analysis, the strategy consists in generating hypotheses that are phonetically similar to the dictated instruction, which will act as probable approximations of the written word (sequence of letters), to cover potential orthographic mistakes by the pupil. To assist the learning process of the pupils, the engine returns different types of real-time feedbacks, that depend on the confidence of the analysis process (confident assessment on errors, warning, or reject).

School-aged children spend 31-60% of their time at school performing handwriting, which is a complex perceptual-motor skill composed of a coordinated combination of fine graphomotor movements. As up to 30% of them experience graphomotor difficulties (GD), timely diagnosis of these difficulties and therapeutic intervention are of great importance. At present, an objective, computerized decision support system for the identification and assessment of GD in school-aged children is still missing. In this study, we propose three novel advanced handwriting parametrization techniques based on modulation spectra, fractional order derivatives, and tunable Q-factor wavelet transform to improve the identification of GD using online handwriting. For this purpose, we analyzed signals acquired from 7 basic graphomotor tasks performed by 53 children attending 3rd and 4th grade at several primary schools around the Czech Republic. Combining the newly proposed features with the conventionally used ones, we were able to identify GD with 84% accuracy. In this study, we showed that using advanced parametrization of basic graphomotor movements can be potentially used to improve our capabilities of quantifying problems with the development of legible, fast-paced handwriting, and help with the early diagnosis of handwriting difficulties frequently manifested in developmental dysgraphia.

International audience; We present, in this paper, a novel paradigm for assessing Alzheimer's disease and aging by analyzing impairment of handwriting (HW) on tablets, a challenging problem that is still in its infancy. The state of the art is dominated by methods that assume a unique behavioral trend for each cognitive profile or age group, and that extract global kinematic parameters, assessed by standard statistical tests or classification models, for discriminating the neuropathological disorders (Alzheimer's (AD), Mild Cognitive Impairment (MCI)) from Healthy Controls (HC), or HC age groups from each other. Our work tackles these two major limitations as follows. First, instead of considering a unique behavioral pattern for each cognitive profile or age group, we relax this heavy constraint by allowing the emergence of multimodal behavioral patterns. We achieve this by performing semi or unsupervised learning to uncover homogeneous clusters of subjects, and then we analyze how much information these clusters carry on the cognitive profiles (or age groups). Second, instead of relying on global kinematic parameters, mostly consisting of their average, we refine the encoding either by a semi-global parameterization, or by modeling the full dynamics of each parameter, harnessing thereby the rich temporal information inherently characterizing online HW. To illustrate the power of our paradigm, we present three studies, one regarding age, and two regarding Alzheimer's. Thanks to our modeling, we obtain new findings that are the first of their kind on this research field. On aging, unlike previous works reporting only one pattern of HW change with age, our study, based on a semiglobal parametrization scheme, uncovers three major aging HW styles, one specific to aged subjects and two shared with other age groups. On Alzheimer's, a striking finding is revealed: two major clusters are unveiled, one dominated by HC and MCI subjects, and one by MCI and ES-AD, thus revealing that MCI patients have fine motor skills leaning towards either HC's or ES-AD's. Our paper introduces also a new temporal representation learning from HW trajectories that uncovers a rich set of features simultaneously like the full velocity profile, size and slant, fluidity, and shakiness, and reveals, in a naturally explainable way, how these HW features conjointly characterize, with fine and subtle details, the cognitive profiles.

International audience; Long short-term memory networks (LSTM) achieves great success in temporal dependency modelling for chain-structured data, such as texts and speeches. An extension towards more complex data structures as encountered in 2D graphic languages is proposed in this work. Specifically, we address the problem of handwritten mathematical expression recognition, using a Tree-based BLSTM architecture allowing the direct labelling of nodes (symbol) and edges (relationship) from a graph modelling the input strokes. One major difference with the traditional approaches is that there is no explicit segmentation, recognition and layout extraction steps but a unique trainable system that produces directly a Stroke Label Graph describing a mathematical expression. The proposed system, considering no grammar, achieves competitive results in online math expression recognition domain.

International audience; We propose an original approach for characterizing early Alzheimer, based on the analysis of online handwritten cursive loops. Unlike the literature, we model the loop velocity trajectory (full dynamics) in an unsupervised way. Through a temporal clustering based on K-medoids, with Dynamic Time Warping as dissimilarity measure, we uncover clusters that give new insights on the problem. For classification, we consider a Bayesian formalism that aggregates the contributions of the clusters, by probabilistically combining the discriminative power of each. On a dataset consisting of two cognitive profiles, Early-stage Alzheimer Disease and Healthy persons, each comprising 27 persons collected at Broca Hospital in Paris, our classification performance significantly outperforms the state of the art, based on global kinematic features

Parkinson&rsquo, s disease dysgraphia affects the majority of Parkinson&rsquo, s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman&rsquo, s and Pearson&rsquo, s) was performed to investigate the relationship between the newly designed features and patients&rsquo, clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features&rsquo, ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients&rsquo, clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment.

11 pages; International audience; This paper proposes an automatic text-independent writer identification framework that integrates an industrial handwriting recognition system, which is used to perform an automatic segmentation of an online handwritten document at the character level. Subsequently, a fuzzy c-means approach is adopted to estimate statistical distributions of character prototypes on an alphabet basis. These distributions model the unique handwriting styles of the writers. The proposed system attained an accuracy of 99.2% when retrieved from a database of 120 writers. The only limitation is that a minimum length of text needs to be present in the document in order for sufficient accuracy to be achieved. We have found that this minimum length of text is about 160 characters or approximately equivalent to 3 lines of text. In addition, the discriminative power of different alphabets on the accuracy is also reported.

International audience; We present the outcome of the latest edition of the CROHME competition, dedicated to on-line handwritten mathematical expression recognition. In addition to the standard full expression recognition task from previous competitions, CROHME 2014 features two new tasks. The first is dedicated to isolated symbol recognition including a reject option for invalid symbol hypotheses, and the second concerns recognizing expressions that contain matrices. System performance is improving relative to previous competitions. Data and evaluation tools used for the competition are publicly available.

International audience; In this paper, we study the problem of symbol knowledge extraction. We assume that some unknown symbols are used to compose a handwritten message, and from a dataset of handwritten samples, we would like to recover the symbol set used in the corresponding language. We applied our approach on online handwriting, and select the domain of numerical expressions, mixing digits and operators, to test the ability to retrieve the corresponding symbol classes. The proposed method is based on three steps: a quantization of the stroke space, a description of the layout of strokes with a relational graph, and the extraction of an optimal lexicon using a minimum description length algorithm. At the symbol level, a recall rate of 74% is obtained on the test dataset produced by 100 writers.

International audience; A competition on recognition of online handwritten mathematical expressions is organized. Recognition of mathematical expressions has been an attractive problem for the pattern recognition community because of the presence of enormous uncertainties and ambiguities as encountered during parsing of the two-dimensional structure of expressions. The goal of this competition is to bring out a state of the art for the related research. Three labs come together to organize the event and six other research groups participated the competition. The competition defines a standard format for presenting information, provides a training set of 921 expressions and supplies the underlying grammar for understanding the content of the training data. Participants were invited to submit their recognizers which were tested with a new set of 348 expressions. Systems are evaluated based on four different aspects of the recognition problem. However, the final rating of the systems is done based on their correct expression recognition accuracies. The best expression level recognition accuracy (on the test data) shown by the competing systems is 19.83% whereas a baseline system developed by one of the organizing groups reports an accuracy 22.41% on the same dataset.

International audience; We present a learning strategy for Hidden Markov Models that may be used to cluster handwriting sequences or to learn a character model by identifying its main writing styles. Our approach aims at learning both the structure and parameters of a Hidden Markov Model (HMM) from the data. A byproduct of this learning strategy is the ability to cluster signals and identify allograph. We provide experimental results on artificial data that demonstrate the possibility to learn from data HMM parameters and topology. For a given topology, our approach outperforms in some cases that we identify standard Maximum Likelihood learning scheme. We also apply our unsupervised learning scheme on on-line handwritten signals for allograph clustering as well as for learning HMM models for handwritten digit recognition.

This study is related to the nuclear waste management and to the long-term behaviour of radionuclides in deep storage. The aim of this paper is to study the corrosion of zirconium in deep storage. In order to simulate the inner side of zircaloy cladding tubes, the zirconium samples were oxidised in air at 500°C during 3 h. Two hundred keV-implanted europium ions were used as marker to follow the evolution of the ZrO2–liquid interface. In a first step, air annealings were performed in the temperature range 850–950°C. Diffusion profiles were measured using 3 MeV alpha-particles Rutherford backscattering spectrometry (RBS) at each step of annealing. By extrapolation it is shown that at 300°C the europium diffusion in ZrO2 is negligible. In a second step, the specimens were exposed to a basic solution (pH 13.5) at 300°C in autoclave under a 107 Pa pressure. By means of RBS, it was possible to deduce the fraction of dissolved ZrO2. It was shown that this dissolution is inhomogeneous. These results are confirmed by scanning electron microscopy (SEM).

Graphomotor disabilities (GD) are present in up to 30% of school-aged children and are associated with several symptoms in the field of kinematics. Although the basic kinematic features such as velocity, acceleration, and jerk were proved to effectively quantify these symptoms, a recent body of research identified that the theory of fractional calculus can be used to even improve the objective GD assessment. The goal of this study is to extend the current knowledge in this field and explore the abilities of several fractional order derivatives (FD) approximations to estimate the severity of GD in the children population. We enrolled 85 children attending the 3rd and 4th grade of primary school, who performed a combined loop task on a digitizing tablet. Their performance was rated by psychologists and the online handwriting signals were parametrised by kinematic features utilising three FD approximations: Grünwald-Letnikov's, Riemann-Liouville's, and Caputo's. In this study, we showed the differences across the employed FD approaches for the same kinematic handwriting features and their potential in GD analysis. The results suggest that the Riemann-Liouville's approximation in the field of quantitative GD analysis outperforms the other ones. Using this approach, we were able to estimate the overall score with a low error of 0.65 points, while the scale range is 4. In fact, the psychologists tend to make the error even higher.