This study aims to investigate how the choice of media form, i.e. printed format, audiobook or reading and audio combined, affect the ability of ESL students to achieve comprehension, and how different ways of asking questions can affect their comprehension ability. Lastly, the study aims to investigate the relationship between comprehension and students’ proficiency levels in their L2. To answer this question, 155 students were recruited and divided into three groups and assigned one type of media form. The quantitative data was collected through an online comprehension test and analysed. The results showed a significant difference between the media forms and revealed that printed reading was superior. However, a printed and audio combination was the most time efficient way for students to achieve comprehension, which indicates that the inclusion of audio does not impede student learning. Previous research in the same field shows that the results are inconclusive, but shares one common conclusion, that students enjoy the audio format. As a result, the educational system should make every effort to media choices for students to choose their preferred media, and more research in the field needs to be done, as students enjoyment leads to increased learning.

The diploma thesis is focused on examination of reading skills in reading printed or digital text from screen by children in elementary school age. Ever younger readers are encountering digital reading at the beginning of the development of their reading skills. Therefore, this work aims to map the effect of screen reading and the differences in the processing and the text comprehension as opposed to printed text. The theoretical part describes the psychological aspects of reading based on Czech and foreign sources. Further, this part summarizes the findings of reading digital text from screen, in particular the factors that affect such reading, its advantages and disadvantages and the processes during this reading. A separate chapter is devoted to reading from the screen by school-age children who were subsequently probands of the research part. The empirical part deals with the verification of several hypotheses. These hypotheses verify differences in reading of printed and digital text of the same difficulty. Proband are elementary school pupils aged 8-10 (n=93, girls n=48, boys n=46). Z. Matějček's Reading Exam was chosen as a method for verifying reading skills, which makes it possible to evaluate the dynamics of reading competencies such as performance in error rate, speed, and comprehension...

Цель работы – разработка автоматической системы распознавания номерных знаков автомобилей, в естественных дорожных условиях, в том числе в сложных погодных и физических условиях, таких как недостаточная видимость, загрязнение, умышленное или непреднамеренное частичное скрытие символов. Объектом исследования являются цифровые изображения автомобилей в естественной среде. Методы исследования: сверточные нейронные сети, в том числе одноэтапные детекторы (SSOD), комбинации сетей с промежуточными связями между слоями - Cross Stage Partial Network (CSPNet) и сети, объединяющей информацию с разных уровней сети – Path Aggregation Network (PANet), преобразования изображений с помощью библиотеки OpenCV, включая фильтры Собеля и Гауса, преобразование Кэнни, методы глубокого машинного обучения для обработки последовательностей LSTM, CRNN, CRAFT. В рамках данной работы разработана система распознавания автомобильных номеров, переводящая графические данные из цифрового изображения или видеопотока в текст в виде файлов различных форматов. Задача детекции автомобильных номеров на изображениях решена с помощью глубокой нейронной сети YoLo v5, представляющая собой современную модель обнаружения объектов, основанную на архитектуре с использованием CSPNet и PANet. Она обеспечивает высокую скорость и точность при обнаружении объектов на изображениях. Благодаря своей эффективности и масштабируемости, YoLov5 стала популярным выбором для решения задач компьютерного зрения в различных областях. Для решения задачи распознавания текса на обнаруженных объектах используется алгоритм детектирования объектов, основанный на преобразованиях Кэнни, фильтрах Собеля и Гаусса и нейронная сеть keras-ocr, на основе фреймворка keras, представляющая собой комбинацию сверточной нейронной сети (CNN) и рекуррентной нейронной сети (RNN), решающая задачу распознавания печатного текста. Созданный метод способен безошибочно распознавать 85 % предоставленных номеров, преимущественно российского стандарта. Полученный функционал может быть внедрен в существующую системы фото- или видео-фиксации трафика и использоваться в рамках цифровизации систем трекинга и контроля доступа и безопасности на дорогах и объектах транспортной инфраструктуры. Выпускная квалификационная работа в теоретической и описательной части выполнена в текстовом редакторе Microsoft Word и представлена в электронном формате. Практическая часть выполнялась в jupiter-ноутбуке на платформе облачных вычислений Google Collaboratory. / The goal of the work is to develop an automatic system for recognizing car license plates in natural road conditions, including difficult weather and physical conditions, such as insufficient visibility, pollution, intentional or unintentional partial hiding of symbols. The object of the study is digital images of cars in their natural environment. Research methods: convolutional neural networks, including single-stage detectors (SSOD), combinations of networks with intermediate connections between layers - Cross Stage Partial Network (CSPNet) and networks that combine information from different levels of the network - Path Aggregation Network (PANet), image transformations using the OpenCV library, including Sobel and Gauss filters, Canny transform, deep machine learning methods for processing LSTM, CRNN, CRAFT sequences. As part of this work, a license plate recognition system has been developed that converts graphic data from a digital image or video stream into text in the form of files in various formats. The problem of detecting license plates in images is solved using the YoLo v5 deep neural network, which is a modern object detection model based on an architecture using CSPNet and PANet. It provides high speed and accuracy in detecting objects in images. Due to its efficiency and scalability, YoLov5 has become a popular choice for solving computer vision problems in various fields. To solve the problem of text recognition on detected objects, an object detection algorithm is used, based on Canny transforms, Sobel and Gaussian filters, and the keras-ocr neural network, based on the keras framework, which is a combination of a convolutional neural network (CNN) and a recurrent neural network (RNN) , which solves the problem of recognizing printed text. The created method is capable of accurately recognizing 85% of the provided numbers, mainly of the Russian standard. The resulting functionality can be implemented into existing systems for photo or video recording of traffic and used as part of the digitalization of tracking systems and access control and security on roads and transport infrastructure facilities. The final qualifying work in the theoretical and descriptive parts was completed in the text editor Microsoft Word and presented in electronic format. The practical part was carried out on a jupiter laptop on the Google Collaboratory cloud computing platform.

v anglickém jazyce Title: How the students of the 9th grades of elementary schools understand the digital versions of texts comparing to the printed ones. Goal: The dissertation aims at drawing the comparaison between the digital and the printed versions of the identical materials. The research was held in a number of selected schools. The materials were presented via personal computers, tablet personal computers and in printed versions. Our goal is to determine the differences among all the three kinds of media in terms of understanding the presented texts. Procedure: The essay is primarily based on the historical and the present days research of comprehensive reading. It also follows the bachelor essay written in 2014. Output: The theoretical processing of basic terms and the review of both the previous and the latest outcomes of the scientific literature. The most important part of the essay were focus on the research and the subsequent assessment of the findings. The research itself was preceded by a pre - research. The essay included the qualitative research which was reached through one of the comprehension test methods. The students were provided with the questionnaire comprising questions. The text was distributed both in the digital and in the printed versions.