Att skriva för hand är en viktig förmåga som används frekvent, både i skolan och i vardagen. Denna kunskapsöversikt syftar till att undersöka aktuell forskning kring skrivundervisning med särskilt intresse för handskrift. Kunskapsöversikten utgår ifrån forskningsfrågan: Vad säger forskningen om hur lärare bör undervisa för att optimera handskriftsutveckling? Genom en tematisk analys har resultatet från tio olika forskningsartiklar sammanställts och analyserats. Artiklarna har valts ut genom en litteratursökning. Resultatet visar att elever behöver få explicit undervisning och träning i skolan för att utveckla och bemästra handskrift. Vidare har handskrift visat sig ha positiva kopplingar till andra literacyrelaterade förmågor, såsom stavning och läsning. Att skriva för hand kräver både motorisk och kognitiv ansträngning och forskningen visar på vikten av att tidigt automatisera handskrift, för att kunna frigöra kognitiva resurser och underlätta för elever att fokusera på textinnehåll. Forskningen pekar även på fördelarna med att använda olika typer av verktyg i skrivundervisning, såsom olika pennor och papper samt digitala verktyg. Kunskapsöversikten har synliggjort vikten av tydlig undervisning i handskrift i lågstadiet, då en automatiserad handskriftsförmåga underlättar literacyutveckling. Därtill ges konkreta exempel på undervisningsmetoder samt både analoga och digitala verktyg som kan stödja lärare i tidig skrivundervisning.

Visualization of three-dimensional vector operations can be very helpful in understanding vector mathematics. However, creating these visualizations using traditional WIMP interfaces can be a troublesome exercise. In this thesis, we present VectorPad, a pen-based application for three-dimensional vector mathematics visualization. VectorPad allows users to define vectors and perform mathematical operations upon them through the recognition of handwritten mathematics. The VectorPad user interface consists of a sketching area, where the user can write vector definitions and other mathematics, and a 3D graph for visualization. After recognition, vectors are visualized dynamically on the graph, which can be manipulated by the user. A variety of mathematical operations can be performed, such as addition, subtraction, scalar multiplication, and cross product. Animations show how operations work on the vectors. We also performed a short, informal user study evaluating the user interface and visualizations of VectorPad. VectorPad's visualizations were generally well liked; results from the study show a need to provide a more comprehensive set of visualization tools as well as refinement to some of the animations.

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This diploma thesis is focused to the writing skills in an initiatory education of a literacy during the first grade at a primary school. The thesis is divided into the theoretical and the practical part. The theoretical part presents the genesis of the handwriting during the history, the physical and psychical expectations for the assumption of the writing skills, the methodics of two learning methods which are used in the czech education system most frequently and last but not least a list of the evaluated scales of handwriting. In the practical part we present the evaluation scale which were constructed for the purpose of this thesis. This scale monitors a plot of the written children's products, an error rate, an ability of corections and a graphical part. We kept at disposal about 500 written children's products which were collected from 22 classes from 9 schools from three parts of Czech Republic (Prague, Central and South Bohemian Region and Moravia). Children which were involved to the project, they were educated by the analytic-synthetic method or the genetic metod for the education of the literacy. The gained data was a part of the another project which were realized under the auspices of IPPP CR. The data was collected twice a one school year. Therefore we can observe a development of...

Handwriting as a unique expression of human behaviour has evoked continuous interest as a means of analyzing and studying personality – a study known as Graphology. Research in graphology has shown diverse results, ranging from negative to highly favourable. Many of the studies disregarding the value of graphology can be criticized on the grounds of their research methodology as well as the method used in handwriting analysis, namely the ‘trait-method’ whereby isolated graphological features are simplistically linked to personality as opposed to a more encompassing, holistic approach. In the present study the special link between personality and graphology is illustrated as well as the decisive influence the gestalt and form standard of a writing have on the interpretation of that particular writing. The approach to the graphological analyses was holistic, as it complements the complex uniqueness of personality. The study also focuses on the recommended methodology of handwriting analysis, by offering a tailor-made personality ‘picture’ of the individual. Graphological findings were compared to the clinical findings of the same client/patient according to the Diagnostic and Statistical Manual of Mental Diseases IV-TRTM (DSM-IV-TRTM). Five (5) case studies have been reported in this qualitative research study. The analyses of the writings were done by an independent graphologist. The objective graphological findings show similarities with clinical findings of the same clients/patients. The holistic approach to handwriting analysis deems to have diagnostic value and is promising in providing guidelines for psychotherapy. Graphology can thus be regarded as a useful and viable tool in psycho-educational assessment. / Education / D.Ed. (Psychology of Education)

The 7 Graphology Factors Scale is standardised for application in industry. A literature study based on the personality theories of Freud, Jung, Murray and Cattell precedes the empirical research. Graphology is conceptualised, relevant existing graphology models are explored and a graphology theory and method for use in industry proposes that graphology should be regulated and applications classified on the same basis as psychometric tests. The empirical research is based on a sample comprising 360 subjects, stratified according to gender, position and language. The written protocol is subject to analysis of 59 characteristics on the basis of the configuration of the writing. language is irrelevant and the presentation of an intervening stimulus is avoided. Factor analysis results in eight secondary factors. These are I Task Orientation - Directive, II Task Orientation- Conscientious, Ill Emotional Stability, IV Extroversion, V Independence, VI Conceptualisation, VII Openness to Experience and VIII Need for Power. Cronbach's alpha coefficient achieves 0, 61 for Factor I. Stepwise regression analysis provides correlations between factors of the Thematic Apperception, Structured Objective Rorschach and 16 Personality Factor tests with all but Factor Ill, which is hypothesised to be a new construct. Five of the factors are hypothesised to align with the Big Five Personality Model. Thr9e-way analysis of variance, t-tests and the Mantei-Haenszel tests are applied to establish differentiation based on the stratification criteria. Norm tables based on Indigenous, English and Afrikaans first language groups are provided in stens and bands. Conclusions are that graphology has a worthy theoretical heritage and offers benefits to the psychological profession and specifically industry, as part of an assessment strategy. limitations are outlined and recommendations are made for further research. / Industrial and Organisational Psychology / D.Litt. et Phil. (Industrial Psychology)

The major reason for this survey was to secure definite facts and opinions from superintendents, principals and teachers in elementary and secondary schools on the subject of handwriting. The information gathered gives conclusive evidence that these groups recognize the need for more and better training in this fundamental subject. The aim of the writer was to determine: The extent to which outlined courses of study in handwriting are followed. The number of special instructors in handwriting, whether part- or full-time and other subjects they teach. Whether handwriting is taught incidentally with other subjects with no formal lessons, or as a separate subject with specific training in handwriting. The number of teachers with special training in handwriting. The number of teachers qualified by teacher’s handwriting certificates. The certification of handwriting teachers from Western Kentucky State Teachers College. The number of teachers holding student certificates in handwriting. The methods of handwriting taught in both the elementary and secondary grades. The number of handwriting supervisors, whether part- or full-time, and other subjects handled. The time devoted to handwriting in years, weeks, days per week, and the length of class period in both elementary and secondary levels. The number of units given for handwriting in high school toward graduation. The requirement for handwriting in high schools. Handwriting facilities – Manuals, scales, charts, alphabetic wall strips, and blackboard instruction. General information – The number of years that handwriting has been established in Kentucky schools; if handwriting will be taught during the year 1936-1937; and if school officials think handwriting should have a definite place in the curriculum as a fundamental subject.

La transcription automatique du texte dans les documents manuscrits a de nombreuses applications, allant du traitement automatique des documents à leur indexation ou leur compréhension. L'une des approches les plus populaires de nos jours consiste à parcourir l'image d'une ligne de texte avec une fenêtre glissante, de laquelle un certain nombre de caractéristiques sont extraites, et modélisées par des Modèles de Markov Cachés (MMC). Quand ils sont associés à des réseaux de neurones, comme des Perceptrons Multi-Couches (PMC) ou Réseaux de Neurones Récurrents de type Longue Mémoire à Court Terme (RNR-LMCT), et à un modèle de langue, ces modèles produisent de bonnes transcriptions. D'autre part, dans de nombreuses applications d'apprentissage automatique, telles que la reconnaissance de la parole ou d'images, des réseaux de neurones profonds, comportant plusieurs couches cachées, ont récemment permis une réduction significative des taux d'erreur.Dans cette thèse, nous menons une étude poussée de différents aspects de modèles optiques basés sur des réseaux de neurones profonds dans le cadre de systèmes hybrides réseaux de neurones / MMC, dans le but de mieux comprendre et évaluer leur importance relative. Dans un premier temps, nous montrons que des réseaux de neurones profonds apportent des améliorations cohérentes et significatives par rapport à des réseaux ne comportant qu'une ou deux couches cachées, et ce quel que soit le type de réseau étudié, PMC ou RNR, et d'entrée du réseau, caractéristiques ou pixels. Nous montrons également que les réseaux de neurones utilisant les pixels directement ont des performances comparables à ceux utilisant des caractéristiques de plus haut niveau, et que la profondeur des réseaux est un élément important de la réduction de l'écart de performance entre ces deux types d'entrées, confirmant la théorie selon laquelle les réseaux profonds calculent des représentations pertinantes, de complexités croissantes, de leurs entrées, en apprenant les caractéristiques de façon automatique. Malgré la domination flagrante des RNR-LMCT dans les publications récentes en reconnaissance d'écriture manuscrite, nous montrons que des PMCs profonds atteignent des performances comparables. De plus, nous avons évalué plusieurs critères d'entrainement des réseaux. Avec un entrainement discriminant de séquences, nous reportons, pour des systèmes PMC/MMC, des améliorations comparables à celles observées en reconnaissance de la parole. Nous montrons également que la méthode de Classification Temporelle Connexionniste est particulièrement adaptée aux RNRs. Enfin, la technique du dropout a récemment été appliquée aux RNR. Nous avons testé son effet à différentes positions relatives aux connexions récurrentes des RNRs, et nous montrons l'importance du choix de ces positions.Nous avons mené nos expériences sur trois bases de données publiques, qui représentent deux langues (l'anglais et le français), et deux époques, en utilisant plusieurs types d'entrées pour les réseaux de neurones : des caractéristiques prédéfinies, et les simples valeurs de pixels. Nous avons validé notre approche en participant à la compétition HTRtS en 2014, où nous avons obtenu la deuxième place. Les résultats des systèmes présentés dans cette thèse, avec les deux types de réseaux de neurones et d'entrées, sont comparables à l'état de l'art sur les bases Rimes et IAM, et leur combinaison dépasse les meilleurs résultats publiés sur les trois bases considérées. / The automatic transcription of text in handwritten documents has many applications, from automatic document processing, to indexing and document understanding. One of the most popular approaches nowadays consists in scanning the text line image with a sliding window, from which features are extracted, and modeled by Hidden Markov Models (HMMs). Associated with neural networks, such as Multi-Layer Perceptrons (MLPs) or Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs), and with a language model, these models yield good transcriptions. On the other hand, in many machine learning applications, including speech recognition and computer vision, deep neural networks consisting of several hidden layers recently produced a significant reduction of error rates. In this thesis, we have conducted a thorough study of different aspects of optical models based on deep neural networks in the hybrid neural network / HMM scheme, in order to better understand and evaluate their relative importance. First, we show that deep neural networks produce consistent and significant improvements over networks with one or two hidden layers, independently of the kind of neural network, MLP or RNN, and of input, handcrafted features or pixels. Then, we show that deep neural networks with pixel inputs compete with those using handcrafted features, and that depth plays an important role in the reduction of the performance gap between the two kinds of inputs, supporting the idea that deep neural networks effectively build hierarchical and relevant representations of their inputs, and that features are automatically learnt on the way. Despite the dominance of LSTM-RNNs in the recent literature of handwriting recognition, we show that deep MLPs achieve comparable results. Moreover, we evaluated different training criteria. With sequence-discriminative training, we report similar improvements for MLP/HMMs as those observed in speech recognition. We also show how the Connectionist Temporal Classification framework is especially suited to RNNs. Finally, the novel dropout technique to regularize neural networks was recently applied to LSTM-RNNs. We tested its effect at different positions in LSTM-RNNs, thus extending previous works, and we show that its relative position to the recurrent connections is important. We conducted the experiments on three public databases, representing two languages (English and French) and two epochs, using different kinds of neural network inputs: handcrafted features and pixels. We validated our approach by taking part to the HTRtS contest in 2014. The results of the final systems presented in this thesis, namely MLPs and RNNs, with handcrafted feature or pixel inputs, are comparable to the state-of-the-art on Rimes and IAM. Moreover, the combination of these systems outperformed all published results on the considered databases.