Abstrakt Syftet med denna studie är att bidra med kunskap om lärares uppfattning och förståelse om och för handskriftens betydelse för elevers finmotoriska utveckling i svenskundervisningen i årskurs 1 – 4.  Detta för att belysa vikten av att fortsätta använda handskriften i skrivinlärningen. I denna studie har en kvalitativ metod använts vid intervjuer av respondenterna. Studiens teori baseras på det sociokulturella perspektivet och pragmatismen. En fenomenografisk forskningsansats har använts i studiens bearbetning, tolkning och analys av den insamlade datan. Resultatet har diskuterats mot bakgrund och tidigare forskning samt utifrån det sociokulturella perspektivet och pragmatismens syn på lärande. Resultatet visar att oavsett vilken metod lärare väljer att använda, handskrift eller tangentsinlärning, behöver eleverna olika övningar för att träna handen och finmotoriken, eftersom eleverna behöver kunna forma bokstäverna för att kunna skriva enkla texter med en läslig handstil. I resultatet framkommer det även att några lärare använder en form av ASL-metod i sin undervisning och är eniga om att handskriften behöver användas parallellt med tangentbordet. / Abstract The purpose of this study is to contribute to the knowledge about teachers´perception and understanding about the significance of the use of handwriting for the development of students' fine motor skills in the Swedish language classroom, in primary school during years 1 – 4. This is to highlight the importance of the continued use of handwriting when learning to write. In this study, a qualitative method was used in the interviews with the respondents. The study's theory is based on the sociocultural perspective and pragmatism. A phenomenographical research approach has been used in the processing, interpretation and analysis of the collected data. The results have been discussed in the context of previous research and based on socio-cultural and pragmatism's views on learning. The results show that regardless of which method teachers choose to use, handwriting or typing, students need complementary exercises to train the hand and the fine motor skills, as students need to be able to shape the letters to be able to write simple texts in a readable handwriting. The results also show that some teachers use some form of iWTR method in their teaching and they agree that handwriting needs to be used in parallel with a keyboard.

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Writing by hand is not that obvious in todays digitalized society, much of what we write and read is digitized. Today we communicate via SMS, email, and social media instead of sending letters and postcards. What do teachers think of handwriting still being a part of the curriculum? Will handwriting survive or is it on its way out?In this survey nine teachers in preschool to sixth grade have been interviewed to hear their opinions about the handwriting, if they think it is important to preserve it. How they motivate their children to write by hand even though the society is digitalized. If they feel that the educational material companies have adapted to the digital curriculum or if there still are good analogue materials available.The results show that the teachers think handwriting is important as there is a connection between brain and hand which is good for learning. The students also practice their fine motor skills when they are allowed to write by hand, which the teachers attach great importance to. / Att skriva för hand är inte lika självklart i dagens digitaliserade samhälle, mycket av det vi skriver och läser är digitalt. Vi kommunicerar idag via sms, mejl och sociala medier i stället för att skriva brev eller vykort. Vad tycker lärarna om att handskriften fortfarande är en del av skolans läroplan? Kommer handskriften att bestå eller är den på väg bort? I denna undersökning har nio lärare i årskurserna förskoleklass till sexan intervjuats för att höra deras åsikter om handskriften, om de tycker att det är viktigt att bevara den. Hur de gör för att motivera sina elever att skriva för hand när vi lever i ett digitalt samhälle. Samt om de känner att läromedelsföretagen anpassat sig efter den digitala läroplanen eller om det fortfarande finns bra analogt material att tillgå.Resultatet visar att lärarna tycker att handskriften är viktig då det finns en koppling mellan hjärna och hand som är bra vid inlärning. Eleverna tränar också på finmotoriken när de får skriva för hand vilket lärarna lägger stor vikt vid.

Automatic machine understanding of documents from image inputs enables many applications in modern document workflows, digital archives of historical documents, and general machine intelligence, among others. Together, the techniques for understanding document images comprise the field of Document Image Analysis (DIA). Within DIA, the research community has identified several sub-problems, such as page segmentation and Optical Character Recognition (OCR). As the field has matured, there has been a trend of moving away from heuristic-based methods, designed for particular tasks and domains of documents, and moving towards machine learning methods that learn to solve tasks from examples of input/output pairs. Within machine learning, a particular class of models, known as deep learning models, have established themselves as the state-of-the-art for many image-based applications, including DIA. While traditional machine learning models typically operate on features designed by researchers, deep learning models are able to learn task-specific features directly from raw pixel inputs.This dissertation is collection of papers that proposes several deep learning models to solve a variety of tasks within DIA. The first task is historical document binarization, where an input image of a degraded historical document is converted to a bi-tonal image to separate foreground text from background regions. The next part of the dissertation considers document segmentation problems, including identifying the boundary between the document page and its background, as well as segmenting an image of a data table into rows, columns, and cells. Finally, a variety of deep models are proposed to solve recognition tasks. These tasks include whole document image classification, identifying the font of a given piece of text, and transcribing handwritten text in low-resource languages.

[pt] O presente projeto trata sobre as letras manuscritas em muros e espaços públicos, com foco no grafite e a pichação como formas de expressão que interagem com os transeuntes, dentro do território urbano. Desde o campo do design e da arte, abordar uma manifestação urbana considerada marginal como objeto de pesquisa, é uma forma de aprofundar nesta pratica como plataforma comunicativa acessível e vigente nas ruas da cidade, que não deixa de existir apesar dos avanços tecnológicos. Propomos então uma classificação dos diversos estilos de letras em grafites e pichações encontrados em áreas determinadas da cidade do Rio de Janeiro, assim como uma visão panorâmica sobre diversos pontos de vista como são o de quem executa (grafiteiros e pichadores), o de quem observa (transeuntes e passantes) e o de quem cuida do território (legislação e entes governamentais). Assim, destacamos o valor desta manifestação dentro da cultura popular, como parte da gráfica urbana brasileira, e a importância do seu registro como um aporte à história da comunicação escrita, no Brasil e na América Latina. Documentar as letras encontradas nos muros da cidade, de maneira efêmera e ao mesmo tempo persistente, é o caminho não só para fazer um levantamento de sua diversidade, mas também é uma forma de entender a maneira em que dialogam com a população, no momento atual em que grafites e pichações se encontram entre a luta pela conquista da rua, a defesa da livre expressão diante os órgãos públicos e a aceitação como parte do cenário de transformação e renovação de espaços públicos. / [en] This project deals with the handwritten letters in walls and public spaces, focusing on graffiti and pichação as forms of expression that interact with pedestrians within the urban territory. From the field of design and art, addressing an urban manifestation considered marginal as an object of investigation, is a way to deepen this practice as an accessible and effective communication platform in the streets of the city, which does not cease to exist in spite of the technological advances. Then, we propose the classification of styles letters in graffiti and pichação found on predetermined areas at Rio de Janeiro. As well, a panoramic view from different viewpoints, such as the one who executes (graffiti makers and pichadores), the one of who observes (passers-by and interns) and the one who takes care of the territory (legislation and government entities). Thus, we highlight the value of this as a manifestation within popular culture, also, as part of the Brazilian urban graphic, and the importance of its registration as a contribution to the history of written communication, in Brazil and in Latin America. Documenting the found letters on the city walls from an ephemeral and persistent way at the same time, created a path not only to uplifting its diversity, but also to understand how they dialogue with the population. In a present moment in which graffiti and pichação are between the fight for the conquest of the street, the defense of the free expression in front of the public organs and the acceptance like part of the scene of transformation and renovation of public spaces.

For a long time, handwriting analysis, such as handwriting recognition and signature verification, has been an active research area. There are two categories of handwriting, online and offline. Online handwriting is captured in real-time on a digital device such as a tablet screen with a stylus pen. In contrast, the handwritten text scanned or captured by a camera from a physical medium such as paper is referred to as offline handwriting. For offline handwriting, the input is limited to handwritten images, making handwriting analysis much more difficult. In our work, we proposed a Stroke Trajectory Recover (STR) for offline and unconstrained handwritten documents. For this purpose, we introduce large-scale word-level annotations for the English handwriting sampled from the IAM-online dataset. The current STR architectures for English handwriting use lines of text or characters of the alphabet as input. However, a word-level STR method estimates loss for each word rather than averaging DTW loss over the entire line of text. Furthermore, to avoid the stray points/artifacts in predicted stroke points, we employ a marginal Chamfer distance that penalizes large, easily noticeable deviations and artifacts. For word detection, we propose the fusion of character region scores with bounding box estimation. Since the character level annotations are not available for handwritten text, we estimate the character region scores in a weakly supervised manner. Character region scores are estimated autonomously from the word’s bounding box estimation to learn the character level information in handwriting. We propose to fuse the character region scores and images to detect words in camera-captured handwriting images. We also propose an automated evaluation to check the quality of the predicted stroke trajectory. The existing handwriting datasets have limited availability of stroke coordinates information. Hence, although the proposed system can be applied to handwriting datasets without stroke coordinates information, it is impossible to evaluate the quality of its predicted strokes using the existing methods. Therefore, in our work, we propose two measures for evaluating the quality of recovered stroke trajectories when ground truth stroke information is not given. First, we formulated an automated evaluation measure based on image matching by finding the difference between original and rendered images. We also evaluated the preservation of readability of words for original and rendered images with a transformer-based word recognition network. Since our proposed STR system works with words, we demonstrate that our method is scalable to unconstrained handwritten documents, i.e., full-page text. Finally, we present a probabilistic diffusion model conditioned on handwriting style template for generating writing strokes. In our work, we propose to learn the localized patches for handwriting style features from multiscale attention network. The multiscale attention network captures fine details about local character style and global handwriting style. Moreover, we train our diffusion model with the Dynamic Time Warping (DTW) loss function, along with the diffusion loss, which eliminates the need to train any auxiliary networks for text or writer style recognition and adversarial networks. / Computer and Information Science

[EN] Mathematical notation is well-known and used all over the world. Humankind has evolved from simple methods representing countings to current well-defined math notation able to account for complex problems. Furthermore, mathematical expressions constitute a universal language in scientific fields, and many information resources containing mathematics have been created during the last decades. However, in order to efficiently access all that information, scientific documents have to be digitized or produced directly in electronic formats. Although most people is able to understand and produce mathematical information, introducing math expressions into electronic devices requires learning specific notations or using editors. Automatic recognition of mathematical expressions aims at filling this gap between the knowledge of a person and the input accepted by computers. This way, printed documents containing math expressions could be automatically digitized, and handwriting could be used for direct input of math notation into electronic devices. This thesis is devoted to develop an approach for mathematical expression recognition. In this document we propose an approach for recognizing any type of mathematical expression (printed or handwritten) based on probabilistic grammars. In order to do so, we develop the formal statistical framework such that derives several probability distributions. Along the document, we deal with the definition and estimation of all these probabilistic sources of information. Finally, we define the parsing algorithm that globally computes the most probable mathematical expression for a given input according to the statistical framework. An important point in this study is to provide objective performance evaluation and report results using public data and standard metrics. We inspected the problems of automatic evaluation in this field and looked for the best solutions. We also report several experiments using public databases and we participated in several international competitions. Furthermore, we have released most of the software developed in this thesis as open source. We also explore some of the applications of mathematical expression recognition. In addition to the direct applications of transcription and digitization, we report two important proposals. First, we developed mucaptcha, a method to tell humans and computers apart by means of math handwriting input, which represents a novel application of math expression recognition. Second, we tackled the problem of layout analysis of structured documents using the statistical framework developed in this thesis, because both are two-dimensional problems that can be modeled with probabilistic grammars. The approach developed in this thesis for mathematical expression recognition has obtained good results at different levels. It has produced several scientific publications in international conferences and journals, and has been awarded in international competitions. / [ES] La notación matemática es bien conocida y se utiliza en todo el mundo. La humanidad ha evolucionado desde simples métodos para representar cuentas hasta la notación formal actual capaz de modelar problemas complejos. Además, las expresiones matemáticas constituyen un idioma universal en el mundo científico, y se han creado muchos recursos que contienen matemáticas durante las últimas décadas. Sin embargo, para acceder de forma eficiente a toda esa información, los documentos científicos han de ser digitalizados o producidos directamente en formatos electrónicos. Aunque la mayoría de personas es capaz de entender y producir información matemática, introducir expresiones matemáticas en dispositivos electrónicos requiere aprender notaciones especiales o usar editores. El reconocimiento automático de expresiones matemáticas tiene como objetivo llenar ese espacio existente entre el conocimiento de una persona y la entrada que aceptan los ordenadores. De este modo, documentos impresos que contienen fórmulas podrían digitalizarse automáticamente, y la escritura se podría utilizar para introducir directamente notación matemática en dispositivos electrónicos. Esta tesis está centrada en desarrollar un método para reconocer expresiones matemáticas. En este documento proponemos un método para reconocer cualquier tipo de fórmula (impresa o manuscrita) basado en gramáticas probabilísticas. Para ello, desarrollamos el marco estadístico formal que deriva varias distribuciones de probabilidad. A lo largo del documento, abordamos la definición y estimación de todas estas fuentes de información probabilística. Finalmente, definimos el algoritmo que, dada cierta entrada, calcula globalmente la expresión matemática más probable de acuerdo al marco estadístico. Un aspecto importante de este trabajo es proporcionar una evaluación objetiva de los resultados y presentarlos usando datos públicos y medidas estándar. Por ello, estudiamos los problemas de la evaluación automática en este campo y buscamos las mejores soluciones. Asimismo, presentamos diversos experimentos usando bases de datos públicas y hemos participado en varias competiciones internacionales. Además, hemos publicado como código abierto la mayoría del software desarrollado en esta tesis. También hemos explorado algunas de las aplicaciones del reconocimiento de expresiones matemáticas. Además de las aplicaciones directas de transcripción y digitalización, presentamos dos propuestas importantes. En primer lugar, desarrollamos mucaptcha, un método para discriminar entre humanos y ordenadores mediante la escritura de expresiones matemáticas, el cual representa una novedosa aplicación del reconocimiento de fórmulas. En segundo lugar, abordamos el problema de detectar y segmentar la estructura de documentos utilizando el marco estadístico formal desarrollado en esta tesis, dado que ambos son problemas bidimensionales que pueden modelarse con gramáticas probabilísticas. El método desarrollado en esta tesis para reconocer expresiones matemáticas ha obtenido buenos resultados a diferentes niveles. Este trabajo ha producido varias publicaciones en conferencias internacionales y revistas, y ha sido premiado en competiciones internacionales. / [CA] La notació matemàtica és ben coneguda i s'utilitza a tot el món. La humanitat ha evolucionat des de simples mètodes per representar comptes fins a la notació formal actual capaç de modelar problemes complexos. A més, les expressions matemàtiques constitueixen un idioma universal al món científic, i s'han creat molts recursos que contenen matemàtiques durant les últimes dècades. No obstant això, per accedir de forma eficient a tota aquesta informació, els documents científics han de ser digitalitzats o produïts directament en formats electrònics. Encara que la majoria de persones és capaç d'entendre i produir informació matemàtica, introduir expressions matemàtiques en dispositius electrònics requereix aprendre notacions especials o usar editors. El reconeixement automàtic d'expressions matemàtiques té per objectiu omplir aquest espai existent entre el coneixement d'una persona i l'entrada que accepten els ordinadors. D'aquesta manera, documents impresos que contenen fórmules podrien digitalitzar-se automàticament, i l'escriptura es podria utilitzar per introduir directament notació matemàtica en dispositius electrònics. Aquesta tesi està centrada en desenvolupar un mètode per reconèixer expressions matemàtiques. En aquest document proposem un mètode per reconèixer qualsevol tipus de fórmula (impresa o manuscrita) basat en gramàtiques probabilístiques. Amb aquesta finalitat, desenvolupem el marc estadístic formal que deriva diverses distribucions de probabilitat. Al llarg del document, abordem la definició i estimació de totes aquestes fonts d'informació probabilística. Finalment, definim l'algorisme que, donada certa entrada, calcula globalment l'expressió matemàtica més probable d'acord al marc estadístic. Un aspecte important d'aquest treball és proporcionar una avaluació objectiva dels resultats i presentar-los usant dades públiques i mesures estàndard. Per això, estudiem els problemes de l'avaluació automàtica en aquest camp i busquem les millors solucions. Així mateix, presentem diversos experiments usant bases de dades públiques i hem participat en diverses competicions internacionals. A més, hem publicat com a codi obert la majoria del software desenvolupat en aquesta tesi. També hem explorat algunes de les aplicacions del reconeixement d'expressions matemàtiques. A més de les aplicacions directes de transcripció i digitalització, presentem dues propostes importants. En primer lloc, desenvolupem mucaptcha, un mètode per discriminar entre humans i ordinadors mitjançant l'escriptura d'expressions matemàtiques, el qual representa una nova aplicació del reconeixement de fórmules. En segon lloc, abordem el problema de detectar i segmentar l'estructura de documents utilitzant el marc estadístic formal desenvolupat en aquesta tesi, donat que ambdós són problemes bidimensionals que poden modelar-se amb gramàtiques probabilístiques. El mètode desenvolupat en aquesta tesi per reconèixer expressions matemàtiques ha obtingut bons resultats a diferents nivells. Aquest treball ha produït diverses publicacions en conferències internacionals i revistes, i ha sigut premiat en competicions internacionals. / Álvaro Muñoz, F. (2015). Mathematical Expression Recognition based on Probabilistic Grammars [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/51665

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Die Universitätsbibliothek Leipzig gehört zu den großen Altbestandsbibliotheken in Deutschland. Sie besitzt eine Vielzahl an bedeutenden Sonderbeständen, die den Rang eines nationalen Kulturerbes einnehmen. <i>Das Buch in Antike, Mittelalter und Neuzeit</i> gibt einen Einblick in die Arbeit mit diesen Sonderbeständen. Von Beiträgen zu Dokumenten aus antiker Zeit, Papyri und Ostraka über Aufsätze zu mittelalterlichen Handschriften bis hin zur neuzeitlichen Handschriftenüberlieferung (Buchhandschriften, aber auch Nachlässe, Autographensammlungen und Stammbücher) werden Erschließungsfragen diskutiert, für die in den letzten Jahren durch die Einbindung EDV-technischer Verfahren neue Lösungsvorschläge erarbeitet wurden. Aufsätze über druckgraphische Sammlungen, Gelehrtenbibliotheken und Schulschriftensammlungen tragen dem zentralen Bestandteil einer jeden Bibliothek, dem gedruckten Buch, und der Geschichte des europäischen Buchdrucks Rechnung. Zudem wird der Erschließung von Sammlungen nichteuropäischer Handschriften, von Texten in den Sprachen Arabisch, Persisch und Türkisch sowie in indischen Sprachen nachgegangen, die die Leipziger Bibliothek vor besondere Herausforderungen stellen. Die Konzentration auf die Dokumentation des Bucherbes hat es nicht verhindert, dass an der Universitätsbibliothek auch andere Medientypen gesammelt wurden. Dazu gehört an erster Stelle die größte universitäre Sammlung in Deutschland von 85.000 Münzen und Medaillen, die nun erstmals vorgestellt wird.

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