Video Text Research Articles

The Application of Graph Neural Network Based on Edge Computing in English Teaching Mode Reform

The latest developments in edge computing have paved the way for more efficient data processing especially for simple tasks and lightweight models on the edge of the network, sinking network functions from cloud to edge of the network closer to users. For the reform of English teaching mode, this is also an opportunity to integrate information technology, providing new ideas and new methods for the optimization of English teaching. It improves the efficiency of English reading teaching, stimulates the interest of English learning, enhances students’ autonomous learning ability, and creates favorable conditions for students’ learning and development. This paper designs a MEC-based GNN (GCN-GAN) user preference prediction recommendation model, which can recommend high-quality video or picture text content to the local MEC server based on user browsing history and user preferences. In the experiment, the LFU-LRU joint cache placement strategy used in this article has a cache hit rate of up to 99%. Comparing the GCN-GAN model with other traditional graph neural network models, it performs caching experiments on the Douban English book data and Douban video data sets. The GCN-GAN model has a higher score on the cache task, and the highest speculation accuracy value F1 can reach 86.7.

The Multimodal Discourse Analysis of Documentary Series “The Firsts in Life”

The images, text, and sound in the documentary video will arouse audiences’ curiosity. The documentary series “The Firsts in Life” integrates multiple modalities, such as images, texts, and sounds, which can express the theme of the documentary in a simple manner. While people are watching a documentary, it is actually a process of decoding symbolic elements in multimodal discourse. After the documentary series “The Firsts in Life” released on CCTV on January 12, 2020, its Douban score has reached 9.2, which has attracted much attention. It is based on Zhang Delu’s Synthetic Theoretical Framework of Multimodal Discourse Analysis and Kress & van Leeuwen’s Visual Grammar. In this paper, we analyze the language mode, para-language mode, bodily mode and non-bodily mode in the video. Moreover, the author explores how the documentary reflects the relationship between characters, such as text, images, and sound, to construct the overall meaning of the documentary.

DISTANCE TECHNOLOGIES IN THE PROCESS OF FORMING THE ENGLISH-LANGUAGE COMPETENCE OF FUTURE ENGINEERS

The urgency of forming English-speaking competence through distance technologies is caused by modern requirements of training specialists in the technical sphere and epidemiological situation in the world. The article aims to define the essence of English-speaking competence formation through distance technologies. In order to realise the defined goal, it is essential to solve the following tasks: 1) to substantiate the essence of the process of formation of English-speaking competence with application of distance technologies; 2) to analyse the specificity of formation of English-speaking competence of future engineers in the course of the use of distance technologies at English lessons. Distance technologies of developing English-speaking competence of future engineers are considered as a set of forms and methods aimed at forming and improving the competence of reading, translation, listening of authentic audio texts, video texts, scientific articles; improving the competence of English-speaking written speech; systematisation of English-speaking grammar competence through tests with the help of distance platforms and Internet-resources. It was found that English-language professional competence integrates knowledge of professional terminological vocabulary, skills of working with English-language professional texts and documents, and background knowledge of socio-cultural features of the language under study. The author proposes goals for the use of distance learning technologies in the course “Academic English”.

L2 comprehension of filled pauses and fillers in unscripted speech

One ubiquitous feature of unscripted speech across languages is the use of filled pauses and fillers. However, research has hitherto yielded opposing or ambiguous results about whether filled pauses and fillers in spontaneous speech aid or hinder L2 listeners' comprehension. In the current study, 30 L1 Japanese participants of three English proficiency levels viewed two video texts spoken by two different speakers of General American English. The video texts contained four occurrences of the filled pause um and two occurrences of the filler like. Participants had multiple opportunities to view and comprehend the texts. Comprehension, decoding, and internal processing of the video texts were elicited through a three-task assessment procedure of L1 recalls, L2 repetitions, and verbal reports. Results revealed that some occurrences of um and like caused comprehension difficulties for most participants while other occurrences caused no noticeable difficulty for any participants. The findings suggest that the location of filled pauses and fillers, L2 listeners’ language proficiency, and multiple other factors can determine whether or not filled pauses and fillers will affect L2 listening comprehension of spontaneous speech.

The Toll of Technology while Working from Home during COVID-19

Drawing from a quota sample of adult Americans (N = 540), this survey explored how individuals who worked from home (WFH) during the COVID-19 pandemic used information communication technologies (ICTs), and the relationship use had with perceived stress. Results suggested that increased use of work-related video chat and text messaging were related to greater stress levels and that, in particular, women with multiple children experienced greater stress while working remotely. Theoretical and practical implications of the findings are discussed concerning remote work in the future.

Early Termination of CU Partition Based on Boosting Neural Network for 3D-HEVC Inter-Coding

As an extension of the High Efficiency Video Coding (HEVC) standard, the 3D-HEVC needs to encode multiple texture videos and depth maps components. In the 3D-HEVC inter-coding test model, a large variety of Coding Unit (CU) sizes are adopted to select the one with the lowest Rate-Distortion (RD) cost as the best CU size. This technique provides the highest achievable coding efficiency, but it brings a huge computational complexity which limits 3D-HEVC from practical applications. In this paper, early termination of CU encoding is proposed to reduce the complexity caused by the CU size splitting process. The proposed algorithm is based on CU homogeneity and a boosting neural network clustering algorithm. The algorithm contained three main steps. The first step is for the extraction of various features from the original encoder. Then, the selection of the features, which had a high correlation with CU partition using a machine learning algorithm. In the second step, a boosting neural network model is used for training the selected features to derive the threshold values for our proposed algorithm. In the final step, an efficient early termination of CU splitting is released for texture videos and depth maps based on the extracted thresholds from the training model. The experimental results show that the proposed algorithm reduces a significant encoding time, while the loss in coding efficiency is negligible.

ResNet CNN with LSTM Based Tamil Text Detection from Video Frames

Text content in videos includes applications such as library video retrievals, live-streaming advertisements, opinion mining, and video synthesis. The key components of such systems include video text detection and acknowledgments. This paper provides a framework to detect and accept text video frames, aiming specifically at the cursive script of Tamil text. The model consists of a text detector, script identifier, and text recognizer. The identification in video frames of textual regions is performed using deep neural networks as object detectors. Textual script content is associated with convolutional neural networks (CNNs) and recognized by combining ResNet CNNs with long short-term memory (LSTM) networks. A residual mapping underlies the network. In ResNet, skipping condenses the network into few layers to help it learn more quickly. Bidirectional LSTM enables networks to always have information about the sequence, backward as well as forward. For fast pattern learning processes, this combination uses the residual learning process. In comparison to the existing approaches, the proposed approach’s results show improved accuracy, precision and F-measures. The combination of ResNet CNNs and bidirectional LSTMs has high recognition rates for detecting video texts in Tamil cursive script.

Machine Learning in OCR Technology: Performance Analysis of Different OCR Methods for Slide-to-Text Conversion in Lecture Videos

A significant percentage of a lecture video's content shown is text. Video text can therefore be a crucial source for automated video indexing. Researchers have recognised printed and handwritten text extracted from pictures using a variety of machine learning techniques and tools before digitising it. A machine learning technology called optical character recognition (OCR) enables us to recognise and retrieve text information from documents, converting it into searchable and editable data. This study primarily focuses on text extraction from lecture slides using Google Cloud Vision (GCV), Tesseract, Abbyy Finereader, and Transym OCR and compares the results to develop a lecture video indexing scheme for the non-linear steering in lecture videos to watch only the interesting points of topics. We have taken a total of 438 key-frames in 10 categories from seven different lecture videos that range in length. First, binary and greyscale versions of the input colour images are created. Before using the OCR APIs, the frames are additionally preprocessed to improve the image quality. The recognition accuracy demonstrated that the GCV OCR performs effectively, saving computing time by collecting image text with the highest accuracy of other tools, 96.7 percent.

ФОРМУВАННЯ КОМУНІКАТИВНОЇ КОМПЕТЕНЦІЇ ЗДОБУВАЧІВ ВИЩОЇ ОСВІТИ ЗАСОБАМИ ВІДЕОТЕХНІК У ПРОЦЕСІ НАВЧАННЯ ІНОЗЕМНИМ МОВАМ

The article describes a detailed analysis of the role of technical teaching aids in teaching foreign languages. Special attention is paid to work with video material: possible problems related to the use of video technologies in foreign language classes are considered; the main stages of working with video texts are distinguished and their tasks are defined; work techniques inherent in each of the stages are described and the most typical types of tasks are formulated.

A Text Tracking Method using Maximally Stable Extremal Regions and Speeded up Robust Features

Content-based image retrieval is an active research area because of vast applications of image and video collections. Text embedded in video can provide significant contribution in identifying the contents of the multimedia data and facilitate the process of video indexing, retrieval and analysis. Text tracking is a vital part of text extraction process. It can speed up the text extraction process and also improves the text localization accuracy for videos. This paper proposes a novel approach for text tracking in videos. This method experimentally proposes Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Features (SURF) descriptors for text tracking in videos. MSER is used for interest point detection as Extremal regions are affine invariant, so it is a suitable option for text tracking which can undergo scale, rotation and translation changes. SURF is chosen as a feature descriptor because of its scale and rotation invariance, speed and robustness. Proposed technique is testified on two datasets; the first is designed to test the tracking methodology as part of this research and second is publicly available Youtube Video Text(YVT) dataset. Succeeding analysis on diverse datasets endures verification to the fact that the projected technique demonstrates visible improvements to text tracking in videos

Vestige of Colonialism? Propaganda in Ghanaian Cinematic Texts

This article primarily examines propaganda in Ghanaian film texts. Based on relevant archival documents, video texts and secondary sources, this study attempts to establish the continuities and changes in filmic propaganda. From a longue durée perspective; this paper’s analysis will be focused on the colonial period through the contemporary post-colonial video film practice era in Ghana. A few dominant thematic concerns in contemporary post-colonial video texts have considerably become synonymous with propaganda, thus, this study argues that propaganda in Ghanaian films has shifted from a political nature to a religious or socio-cultural form. The specific narratives of Pentecostal films and their occult genres have frequently presented new identities in Ghanaian video film texts. In this article, the texts of a few selected colonial and contemporary post-colonial films will be critically read to examine the vicissitudes of filmic propaganda. This study concludes that the narratives in contemporary post-colonial films considerably reaffirm the colonial stereotypes about Africans.

Post-Coloniality: Projection of Ghana In Video-Films

The rise of the mainstream video industry has been significant towards socio-cultural and economic development in Ghana; however, this study will not focus on the impacts of the video industry of Ghana. This article primarily examines the image construction of Ghana in video-films. Over the past few years, videofilms in Post-colonial Ghana have often been critiqued by film scholars and critics for reinforcing superstitious beliefs and instigating backward tendencies that derail national development. Normative scholarships have critically explored the visuality of Ghanaian video-films and their themes. Nonetheless, these normative scholarships have often overlooked the nexus between the Ghanaian society and video texts. It is against this scholarly gap that this study engages the meta-question of how video-films project Ghana in their texts. This article will engage a critical textual reading of a few popular films from the Pentecostal and Occult genres to contextualize the ideological sub-texts and the image construction of Ghana in these selected video-films. I argue that major ‘postmodern’ thematic concerns in Ghanaian video-films considerably denigrate and malign Ghana’s image, as well as neglect issues of national interests.

Detection of Text in Videos Using Discrete Wavelet Transform and Gradient Difference

Text detection in video frames provide highly condensed information about the content of the video and it is useful for video seeking, browsing, retrieval and understanding video text in large video databases. In this paper, we propose a hybrid method that it automatically detects segments and recognizes the text present in the video. Detection is done by using laplacian method based on wavelet and color features. Segmentation of detected text is divided into two modules Line segmentation and Character segmentation. Line segmentation is done by using mathematical statistical method based on projection profile analysis. In line segmentation, multiple lines of text in video frame obtained from text detection are segmented into single line. Character segmentation is done by using Connected Component. Analysis (CCA) and Vertical Projection Profile Analysis. The input for character segmentation is the line of text obtained from line segmentation, in which all the characters in the line are segmented separately for recognition. Optical character recognition is Processed by using template matching and correlation technique. Template matching is performed by comparing an input character with a set of templates, each comparison results in a similarity measure between the input characters with a set of templates. After all templates have been compared with the observed character image, the character’s identity is assigned with the most similar template based on correlation. Eventually, the text in video frame is detected, segmented, and processed to OCR for recognition.

Laurie Anderson’s Transmedia Storytelling

Laurie Anderson’s Transmedia Storytelling

Intelligent Detection Method of English Text in Natural Scenes in Video

With the rapid development of Internet technology, breakthroughs have been made in all branches of computer vision. Especially in image detection and target tracking, deep learning techniques such as convolutional neural networks have achieved excellent results. In order to explore the applicability of machine learning technology in the field of video text recognition and extraction, a YOLOv3 network based on multiscale feature transformation and migration fusion is proposed to improve the accuracy of english text detection in natural scenes in video. Firstly, aiming at the problem of multiscale target detection in video key frames, based on the YOLOv3 network, the scale conversion module of STDN algorithm is used to reduce the low-level feature map, and a backbone network with feature reuse is constructed to extract features. Then, the scale conversion module is used to enlarge the high-level feature map, and a feature pyramid network (FPN) is built to predict the target. Finally, the improved YOLOv3 network is verified to extract key text from images. The experimental results show that the improved YOLOv3 network can effectively improve the false detection and missed detection caused by occlusion and small target, and the accuracy of English text extraction is obviously improved.

Study of compression statistics and prediction of rate-distortion curves for video texture

Encoding textural content remains a challenge for current standardised video codecs. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimise encoding performance. In this paper, we analyse the spatio-temporal features and statistics of video textures, explore the rate-quality performance of different texture types and investigate models to mathematically describe them. For all considered theoretical models, we employ machine-learning regression to predict the rate-quality curves based solely on selected spatio-temporal features extracted from uncompressed content. All experiments were performed on homogeneous video textures to ensure validity of the observations. The results of the regression indicate that using an exponential model we can more accurately predict the expected rate-quality curve (with a mean Bjøntegaard Delta rate of .46% over the considered dataset), while maintaining a low relative complexity. This is expected to be adopted by in the loop processes for faster encoding decisions such as rate–distortion optimisation, adaptive quantisation, partitioning, etc.

LVTIA: A new method for keyphrase extraction from scientific video lectures

Due to the growth of technology, the expansion of communication infrastructure and crises of COVID-19 pandemic, e-learning and virtual education is expanding. One of the best ways to access and organize these information is indexing using automatic intelligent methods. Indexing requires assigning keywords or keyphrases to each video, to represent its content. The main focus of this research is to propose an approach by which appropriate keyphrases are assigned to scientific video lectures. For this purpose, a new algorithm called LVTIA, Lecture Video Text mining-base Indexing Algorithm, is proposed in which the textual content of video frames along with the text extracted from audio signal are merged together, and a new keyphrase extraction method is proposed. The proposed method considers new local and global features for each candidate phrases, along with a new feature reflecting the occurrence of each phrase in the audio signals or video frames. The method is implemented using five distinct data sets in English and Persian. The results are evaluated based on precision, recall, F1-measure and MAP@K metrics and compared with some of the well-known keyphrase extraction algorithms. Based on the results, the best MAP@K for English videos is related to LVTIA algorithm with the values of, 0.7912, 0.8069, 0.8069 for k=5,10,15, respectively. In addition, LVTIA is able to provide best MAP@K for Persian videos which are 0.6367, 0.6866, 0.6874 for k=5,10,15, respectively. According to Friedman nonparametric statistical test, the performance of different algorithms in precision, recall, F1-measure metrics, are statistically different from LVTIA as well.

Agile Support Vector Machine for Energy-efficient Resource Allocation in IoT-oriented Cloud using PSO

Over the years cloud computing has seen significant evolution in terms of improvement in infrastructure and resource provisioning. However the continuous emergence of new applications such as the Internet of Things (IoTs) with thousands of users put a significant load on cloud infrastructure. Load balancing of resource allocation in cloud-oriented IoT is a critical factor that has a significant impact on the smooth operation of cloud services and customer satisfaction. Several load balancing strategies for cloud environment have been proposed in the past. However the existing approaches mostly consider only a few parameters and ignore many critical factors having a pivotal role in load balancing leading to less optimized resource allocation. Load balancing is a challenging problem and therefore the research community has recently focused towards employing machine learning-based metaheuristic approaches for load balancing in the cloud. In this paper we propose a metaheuristics-based scheme Data Format Classification using Support Vector Machine (DFC-SVM), to deal with the load balancing problem. The proposed scheme aims to reduce the online load balancing complexity by offline-based pre-classification of raw-data from diverse sources (such as IoT) into different formats e.g. text images media etc. SVM is utilized to classify “n” types of data formats featuring audio video text digital images and maps etc. A one-to-many classification approach has been developed so that data formats from the cloud are initially classified into their respective classes and assigned to virtual machines through the proposed modified version of Particle Swarm Optimization (PSO) which schedules the data of a particular class efficiently. The experimental results compared with the baselines have shown a significant improvement in the performance of the proposed approach. Overall an average of 94% classification accuracy is achieved along with 11.82% less energy 16% less response time and 16.08% fewer SLA violations are observed.

The Movement for Democratic Change (MDC) music videos and the mutation of the Chimurenga genre in Zimbabwe

ABSTRACT This paper explores how musicians sympathetic to MDC appropriate, exploit, and ultimately seem to fall victim to the narrative traps present in Chimurenga music and its role in Zimbabwe’s political history. In this paper MDC’s Chimurenga music videos and songs are analyzed to bring out deep lying meanings and placed in a historic context. The paper analyses how the visual and lyrical content contest the ‘authorized’ version of Chimurenga to the extent of creating a new understanding of Chimurenga discourse and Chimurenga music. The selected video text helps identify lines of appropriation and contamination/mutation as it traces and contextualizes the evolution of the MDC war song up to a point it generically becomes similar to the traditions of Chimurenga established in the 70s. It is crucial to look at how a sense of ‘memory’ is ‘extracted’ as well as how it is re/packaged, re/distributed and received or contested in the service of MDC’s counter hegemonic propaganda. In this vein, we do not seek just to point moments or points of appropriation but also instances of mutation/violation that the MDC music videos usher into the narrative of the Chimurenga discourse especially in a post-colonial context.

ETDR: An Exploratory View of Text Detection and Recognition in Images and Videos

Images and videos with text content are a direct source of information. Today, there is a high need for image and video data that can be intelligently analyzed. A growing number of researchers are focusing on text identification, making it a hot issue in machine vision research. Since this opens the way, several real-time-based applications such as text detection, localization, and tracking have become more prevalent in text analysis systems. To find out more about how text information may be extracted, have a look at our survey. This study presents a trustworthy dataset for text identification in images and videos at first. The second part of the article details the numerous text formats, both in images and video. Third, the process flow for extracting information from the text and the existing machine learning and deep learning techniques used to train the model was described. Fourth, explain assessment measures that are used to validate the model. Finally, it integrates the uses and difficulties of text extraction across a wide range of fields. Difficulties focus on the most frequent challenges faced in the actual world, such as capturing techniques, lightning, and environmental conditions. Images and videos have evolved into valuable sources of data. The text inside the images and video provides a massive quantity of facts and statistics. However, such data is not easy to access. This exploratory view provides easier and more accurate mathematical modeling and evaluation techniques to retrieve the text in image and video into an accessible form.