L’exprérience d’enseignement et l’examen des mémoires en français des étudiants en Agroaliemtaire ont montré que nos apprenants produisaient souvent des phrases longues et peu compréhensibles à cause des erreurs morphosyntaxiques. En conséquence, les mémoires sont peu lisibles sur le plan linguistique. <p>De plus, nous remarquons des traces de la langue vietnamienne dans la production en français des étudiants. Or, les apprenants sont obligés de consulter les articles scientifiques en vietnamien de leurs enseignants lors de la préparation du mémoire. De plus, l’étude des articles montre que les auteurs formulent aussi des phrases très longues de plusieurs informations.<p>Ainsi, toutes ces constatations nous orientent vers une analyse contrastive des phrases longues en vietnamien des scientifiques avec celles trouvées dans les mémoires en français des étudiants. <p>Selon notre revue de littérature des recherches précédentes, des auteurs prédécesseurs mesurent la lisibilité d’un texte en se basant statistiquement sur la familiarité du vocabulaire, la longueur des mots, la longueur des phrases, ou la longueur des sous-phrases. <p>Toutefois, la mesure par le comptage du nombre de mots par phrase des auteurs semble inappropriée à notre travail par la différence des objectifs. <p>Nous avons donc essayé de trouver une unité de mesure de la longueur des phrases pertinente à notre propre corpus :« informations enchâssées ».<p>Selon les auteurs prédécesseurs, une phrase sera vue comme longue si elle contient plus de trois sous-phrases. Quant à nous, les phrases seront jugées longues si elles dépassent trois informations enchâssées. <p>Après la collecte des phrases longues, nous avons utilisé l’approche qualitative pour les analyser. Après l’analyse du corpus, nous avons obtenu des résultats suivants :la production des phrases longues ainsi que la présence des erreurs morphosyntaxiques dues à l’interférence du vietnamien constituent des caractéristiques typiques des mémoires des étudiants francophones à Can Tho. Ce sont ces traits représentatifs qui ont compromis la lisibilité des phrases de nos apprenants.<p>Face aux difficultés de nos apprenants, nous essayons de trouver quelques esquisses didactiques adéquates à notre propre public.<p> / Doctorat en Langues et lettres / info:eu-repo/semantics/nonPublished

Text is no longer confined to scanned pages and often appears in camera-based images originating from text on real world objects. Unlike the images from conventional flatbed scanners, which have a controlled acquisition environment, camera-based images pose new challenges such as uneven illumination, blur, poor resolution, perspective distortion and 3D deformations that can severely affect the performance of any optical character recognition (OCR) system. Due to the variations in the imaging condition as well as the target document type, traditional OCR systems, designed for scanned images, cannot be directly applied to camera-captured images and a new level of processing needs to be addressed. In this thesis, we study some of the issues commonly encountered in camera-based image analysis and propose novel methods to overcome them. All the methods make use of color connected components. 1. Connected component descriptor for document image mosaicing Document image analysis often requires mosaicing when it is not possible to capture a large document at a reasonable resolution in a single exposure. Such a document is captured in parts and mosaicing stitches them into a single image. Since connected components (CCs) in a document image can easily be extracted regardless of the image rotation, scale and perspective distortion, we design a robust feature named connected component descriptor that is tailored for mosaicing camera-captured document images. The method involves extraction of a circular measurement region around each CC and its description using the angular radial transform (ART). To ensure geometric consistency during feature matching, the ART coefficients of a CC are augmented with those of its 2 nearest neighbors. Our method addresses two critical issues often encountered in correspondence matching: (i) the stability of features and (ii) robustness against false matches due to multiple instances of many characters in a document image. We illustrate the effectiveness of the proposed method on camera-captured document images exhibiting large variations in viewpoint, illumination and scale. 2. Font and background color independent text binarization The first step in an OCR system, after document acquisition, is binarization, which converts a gray-scale/color image into a two-level image -the foreground text and the background. We propose two methods for binarization of color documents whereby the foreground text is output as black and the background as white regardless of the polarity of foreground-background shades. (a) Hierarchical CC Analysis: The method employs an edge-based connected component approach and automatically determines a threshold for each component. It overcomes several limitations of existing locally-adaptive thresholding techniques. Firstly, it can handle documents with multi-colored texts with different background shades. Secondly, the method is applicable to documents having text of widely varying sizes, usually not handled by local binarization methods. Thirdly, the method automatically computes the threshold for binarization and the logic for inverting the output from the image data and does not require any input parameter. However, the method is sensitive to complex backgrounds since it relies on the edge information to identify CCs. It also uses script-specific characteristics to filter out edge components before binarization and currently works well for Roman script only. (b) Contour-based color clustering (COCOCLUST): To overcome the above limitations, we introduce a novel unsupervised color clustering approach that operates on a ‘small’ representative set of color pixels identified using the contour information. Based on the assumption that every character is of a uniform color, we analyze each color layer individually and identify potential text regions for binarization. Experiments on several complex images having large variations in font, size, color, orientation and script illustrate the robustness of the method. 3. Multi-script and multi-oriented text extraction from scene images Scene text understanding normally involves a pre-processing step of text detection and extraction before subjecting the acquired image for character recognition task. The subsequent recognition task is performed only on the detected text regions so as to mitigate the effect of background complexity. We propose a color-based CC labeling for robust text segmentation from natural scene images. Text CCs are identified using a combination of support vector machine and neural network classifiers trained on a set of low-level features derived from the boundary, stroke and gradient information. We develop a semiautomatic annotation toolkit to generate pixel-accurate groundtruth of 100 scenic images containing text in various layout styles and multiple scripts. The overall precision, recall and f-measure obtained on our dataset are 0.8, 0.86 and 0.83, respectively. The proposed method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset, which, however, has only horizontal English text. The overall precision, recall and f-measure obtained are 0.63, 0.59 and 0.61 respectively, which is comparable to the best performing methods in the ICDAR 2005 text locating competition. A recent method proposed by Epshtein et al. [1] achieves better results but it cannot handle arbitrarily oriented text. Our method, however, works well for generic scene images having arbitrary text orientations. 4. Alignment of curved text lines Conventional OCR systems perform poorly on document images that contain multi-oriented text lines. We propose a technique that first identifies individual text lines by grouping adjacent CCs based on their proximity and regularity. For each identified text string, a B-spline curve is fitted to the centroids of the constituent characters and normal vectors are computed along the fitted curve. Each character is then individually rotated such that the corresponding normal vector is aligned with the vertical axis. The method has been tested on a data set consisting of 50 images with text laid out in various ways namely along arcs, waves, triangles and a combination of these with linearly skewed text lines. It yields 95.9% recognition accuracy on text strings, where, before alignment, state-of-the-art OCRs fail to recognize any text. The CC-based pre-processing algorithms developed are well-suited for processing camera-captured images. We demonstrate the feasibility of the algorithms on the publicly-available ICDAR 2003 robust reading competition dataset and our own database comprising camera-captured document images that contain multiple scripts and arbitrary text layouts.

The diploma thesis deals with the book genre Erstlesebuch (book for beginner readers) as educational material in the context of foreign language education at the levels of Basic Users (A0/1-A2). In the context of first language teaching, this specific genre belongs to the field of literature, the didactic of literature, and the didactic of reading. The goal of this diploma thesis is to present different possibilities of didactic activities with Erstlesebuch in the context of second language teaching and to formulate criteria for choosing an appropriate text. The theoretical part describes the context of foreign language education and the role of literature in this context, with consideration for the Common European Framework of Reference for Languages. The theoretical part also presents the specifics of the book genre Erstlesebuch itself. The empirical part of this thesis is based on several investigations: firstly on a complex analysis (readability and text structure) of six Erstlesebuch and secondly on empirical research in reading with schoolchildren at higher grades of an elementary school (children at the age of 13-15). These results are compared to the results of similar research done with Austrian beginner readers (children between the ages of 7 and 9). The final part of the thesis concludes...

In disseminating information about health issues, government health departments and NGOs use, inter alia, written health texts. In a country like South Africa, these texts are generally written by medical experts and thereafter translated into the languages of the people. One of these languages is Zulu, which is spoken by the majority of South Africans. A large percentage of Zulu speakers are illiterate or semi-literate, especially in the rural areas. For this reason, Zulu translators have to use ‘simple’ language that these readers would understand when translating English texts into Zulu. Translators are expected to use strategies that can deal with non-lexicalized, problematic or other related terms that appear in health texts, as well as geographical and cultural constraints. This study focuses on the strategies used by Zulu translators in an attempt to make translated Zulu health texts accessible to the target readership. The investigation includes the use of self-administered questionnaires for respondents from two of South Africa’s nine provinces, where Zulu speakers are found (Gauteng and KwaZulu-Natal), to determine whether the health texts do reach the target readership. Focus groups, semi-structured interviews and other complementary techniques were used to collect data from the selected respondents. Furthermore, a parallel concordance called ParaConc was used to extract and analyse data from the corpus as compiled for the present study, in an attempt to investigate the strategies used to make the translated health texts easier to read. The study uncovers various strategies which are used when translating English health texts into Zulu. These strategies include the use of loan words, paraphrasing, cultural terms and so on. In future, the use of ParaConc can be broadened to investigate newly discovered translation strategies, with the aim of making health texts more accessible to the target readers. Furthermore, this software programme can also be used to study translation strategies as used in other types of texts, for example journalistic texts. / Linguistics / D. Litt. et Phil. (Linguistics (Translation Studies))

Mitt masterprojekt är en bok som syftar till att stötta anhöriga till personer med förvärvad hjärnskada, där jag använder mig själv och min egen rehabilitering efter en smitningsolycka som fallstudie. / My master project is a book that aims to support relatives of people with aquired brain injuries, where I use myself and my own rehabilitetion after a hit-and-run accident as a case study.

In disseminating information about health issues, government health departments and NGOs use, inter alia, written health texts. In a country like South Africa, these texts are generally written by medical experts and thereafter translated into the languages of the people. One of these languages is Zulu, which is spoken by the majority of South Africans. A large percentage of Zulu speakers are illiterate or semi-literate, especially in the rural areas. For this reason, Zulu translators have to use ‘simple’ language that these readers would understand when translating English texts into Zulu. Translators are expected to use strategies that can deal with non-lexicalized, problematic or other related terms that appear in health texts, as well as geographical and cultural constraints. This study focuses on the strategies used by Zulu translators in an attempt to make translated Zulu health texts accessible to the target readership. The investigation includes the use of self-administered questionnaires for respondents from two of South Africa’s nine provinces, where Zulu speakers are found (Gauteng and KwaZulu-Natal), to determine whether the health texts do reach the target readership. Focus groups, semi-structured interviews and other complementary techniques were used to collect data from the selected respondents. Furthermore, a parallel concordance called ParaConc was used to extract and analyse data from the corpus as compiled for the present study, in an attempt to investigate the strategies used to make the translated health texts easier to read. The study uncovers various strategies which are used when translating English health texts into Zulu. These strategies include the use of loan words, paraphrasing, cultural terms and so on. In future, the use of ParaConc can be broadened to investigate newly discovered translation strategies, with the aim of making health texts more accessible to the target readers. Furthermore, this software programme can also be used to study translation strategies as used in other types of texts, for example journalistic texts. / Linguistics and Modern Languages / D. Litt. et Phil. (Linguistics (Translation Studies))