Text information extraction algorithm of video images in multimedia environment

Automated text recognitions as of innate pictures has received a wide notice with latent application during picture recovery, robotic as well as intellectual transportation systems. Text data into innate scenery pictures are an vital source of several picture-based application like automatic geo-coding, assistive navigation, contents-based picture recovery as well as scene understanding. Conversely, segmenting texts as of a complicated backgrounds through millions of color are still date challenge to scientist. With the availability of digital cameras with higher resolution, image analysis has become a real-time possibility. Numerous approaches have been developed for extracting text information from camera-captured text pictures as well as the presence of commercialized optical character recognition (OCR) systems have supported a lot. This paper deals with an extensive survey on all the literatures that have been presented till date in this regard of extraction of text from real-time scenes.

Extraction of textual information is a very fundamental step in the domain of information retrieval, searching, editing, documenting, archiving, or recording the appearance of text. Appearance of text means text information is embedded in the image. Textual information can be obtained from various forms like scanners, magazines, newspapers, etc. In this survey paper, an elaborative review on text extraction is given, which includes different classifiers, feature extraction and segmentation techniques.

The extraction of text information from traffic panels is one of the challenging problems in computer vision. Although the past decade has seen a promising shift and important progress in object detection, few works and a limited number of datasets focus specifically on extracting text from traffic signs. To address the lack of data for text detection in traffic panels, especially those with Arabic scripts, this paper introduces a new multilingual and multipurpose dataset named ASAYAR. It consists of three sub-datasets: Arabic-Latin scene text localization, traffic sign detection, and directional symbol detection. The dataset contains 1763 images collected on different Moroccan highways, and annotated manually, using 16 object categories. The fully annotated ASAYAR images contains more than 20000 bounding box objects. The paper also investigates the usability of the dataset, by evaluating the performance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state-of-the-art</i> algorithms for object and text detection. Experimental results show good detection scores, demonstrating the potential contribution of ASAYAR in the development of methods for text extraction from traffic panels.

In recent years much has been written about the use of the WWW (World Wide Web) or the Internet as a global information resource. Initially a primarily static textual and graphical information resource, developments in both the hardware and software technology have facilitated the delivery of information in a multimedia environment, including the use of animation, video and, most recently, interactive spatial information in the form of maps. In 1996, a Forth Estuary Forum Pilot Information System was developed. Provision of a simple navigational interface offers access to a wide range of information presented in the form of text and graphics. With the recent developments in HTML, Javascript and JAVA applets, considerable potential now exists to extend this type of information system into a low-level Geographical Information System (GIS). In 1997 through 1998, the FEF Info System went ‘live’. Further developments, whilst the system is being updated, expanded, and enhanced, will ultimately aim to offer users a more dynamic form of information delivery, using the latest developments in Internet map servers. This chapter considers the use of the Internet and the accompanying information browsers as the basis for constructing an environmental information system using the FEF project at the University of Aberdeen as an example.KeywordsGeographical Information SystemCoastal Zone ManagementInformation DeliveryInternet BrowserEnglish NatureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Ever increasing volumes of media content and the desire to extract information from media archives motivate the studies into semantic audio information mining. Much research in this filed concerns development of bespoke systems, in which soundtracks are exclusively classified and segmented, and a specific type of sound is recognized and analyzed. This approach however is detrimental to the complete extraction of all relevant semantic information and audio scene analysis. The current study addresses the issues of soundtracks with overlapped music, speech and ambient sounds, and explores how MARSYAS (Music Analysis, Retrieval and Synthesis for Audio Signals) can be extended to mixed and overlapped soundtrack applications. The MARSYAS has been adapted to this application by means of adopting additional speech cleaning algorithms. The proposed new system can analyze arbitrary soundtracks and timestamp the occurrence of music and speech, allowing overlaps, in the form of a “sound score” for further recognition methods to extract music score and text information. Validation tests have shown that the new system handles overlapping cases and is therefore capable of extracting more information than other existing methods.

The expansive growth of information on the Internet has led to new developments in computer vision technology and image processing techniques. Since stone inscriptions are subject to erosion and polishing by the external environment for years, it is difficult to extract image and text information. In this study, the fuzzy control theory is combined with edge detection technology for image edge detection. Firstly, a suitable fuzzy rule and affiliation function are set, then a fuzzy control system is used to extract and detect the image edge information, and then a fuzzy logic rule-based edge detection algorithm is proposed to detect the inscription images. To test the performance of the algorithm, the detection effect of the image is first analyzed from a subjective perspective. The experimental results show that the proposed algorithm has better edge detection for both inscription and lena images, with better noise suppression without excessive distortion, and clearer inscription images. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 33.84 when the detection object is the lena image a. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 41.26 when the detection object is the lena image b. The proposed algorithm has the highest PSNR value of 33.84 when the detection object is the lena image b. In summary, the analysis of both subjective and objective indicators shows that the inscription image processing algorithm used in this paper has better processing effect, and the processed images become clearer with less distortion, which is helpful for both inscription image and text extraction.

Automatic extraction of text information plays an important role in machine translation, knowledge mapping and other fields. In recent years, with the rapid development of computer technology and the popularization of Internet application, the resources acquired by people through the Internet show explosive growth. Facing the massive information resources, how to extract the required information quickly and effectively and convert it into structured data has become a hot topic of current research. Based on this, this paper proposes a automatic topic-oriented text extraction method combining BiLSTM and CRF models. This method firstly establishes the text extraction topic, then carries on the automatic entity recognition to the data related topic, finally forms the standard structured data, thereby realizes the unstructured text data to the specific structure of the information block, which lays the foundation for knowledge mining. Taking the data collected in ACL 2018 Chinese NER as the test data, the precision of our algorithm is 95.74%. Compared with the traditional neural network method, our text information extraction method can effectively identify more entity information in the text data, and improve its effect in practical application.

A fast and simple PFAS extraction method utilizing HR–CS–GFMAS for soil samples

In multimedia environments, digital artwork is usually exhibited on many types of media with different sizes and resolutions. For this reason, it is desirable that artists create works of art in a resolution independent way. We present an algorithm for resolution independent unrealistic imaging. To realize resolution independent imaging, characteristics of all brush strokes are coded and saved in a characteristic table. From the characteristic table, images of any size and resolution can be created. This characteristic table gives our algorithm the ability to create many types of artistic expression. Three different expressions are exhibited. In particular the expression in which contours are drawn by pen and ink then colored by brush is, we believe, a new type of expression in computer artwork. Another feature of our algorithm is that the shape of a brush stroke is not set, but is adaptively changed: a less mechanical pattern which seems more natural to humans. As for aesthetic evaluation, we do not have a proper evaluation method, but if an image created using this algorithm won an award for digital creation, that would be an indication of the algorithm's success.

Image rotation algorithm in image processing has been widely used in engineering applications, and a practical traffic video image rotation algorithm is helpful for the traffic monitoring system to analyze the video images, which would promote visualization and intelligent transportation system applications development. Based on traffic engineering practice, this paper mainly studies an image rotation algorithm of traffic video sequence, by different angles rotation and different ratios zoom of image in the region of interest and necessary interpolation operation, which can change the original irregular target region image into a rules pattern which will have the same height on the left and right sides as well as the same width on the top and bottom sides. So that the original target region uneven simple image changes into coherent and rules image, and image information is simplified widely, it's very useful for simple and efficient detection algorithm development in traffic video detection systems. Compared with the traditional image rotation algorithm, this paper uses methods of image rotation, image zoom and interpolation to regularize the image, it's closer to engineering practice and more practical in video-based traffic system, so it's innovative.

Efficient access to information contained in online scientific literature collections is essential for life science research, playing a crucial role from the initial stage of experiment planning to the final interpretation and communication of the results. The biological literature also constitutes the main information source for manual literature curation used by expert-curated databases. Following the increasing popularity of web-based applications for analyzing biological data, new text-mining and information extraction strategies are being implemented. These systems exploit existing regularities in natural language to extract biologically relevant information from electronic texts automatically. The aim of the BioCreative challenge is to promote the development of such tools and to provide insight into their performance. This review presents a general introduction to the main characteristics and applications of currently available text-mining systems for life sciences in terms of the following: the type of biological information demands being addressed; the level of information granularity of both user queries and results; and the features and methods commonly exploited by these applications. The current trend in biomedical text mining points toward an increasing diversification in terms of application types and techniques, together with integration of domain-specific resources such as ontologies. Additional descriptions of some of the systems discussed here are available on the internet .

We present a system for automatic Chinese business card recognition. Chinese business card is characterized by its complex layout - Chinese-English mixed character sets and diverse typesetting. Our goal is to extract meaningful information item from the scanned image, such as "holder-name", "title", "address", etc. The automatic recognition task is divided into three stages: 1) document geometrical layout analysis; 2) bilingual character recognition; 3) text understanding and information extraction. The final results of this system are stored in database for later use. The whole system has been tested with card images scanned at 400 dpi and results are reported.

The Special Interest Group (SIG) on Text Mining (or BioLINK — Biological Literature, Information and Knowledge; http://www.pdg.cnb.uam.es/BioLINK/) was created to address the need for communication and interchange of ideas in the field of text mining and information extraction applied to biology and biomedicine. Information extraction (IE) is an outgrowth of work in automated natural language processing, which began in the 1950s with work on transformational grammar by Zellig Harris [5,6] and later Noam Chomsky [3,4]. Information extraction technology made rapid progress starting in the late 1980s, thanks to a series of conferences focused on evaluation of IE: the Message Understanding Conferences [1]. There is also a long history of research on applications in medicine. Applications to the medical field focus on two distinct sub-problems: improved access to the medical literature and extraction of information from patient records. Despite these successes in other fields, natural language processing (NLP) techniques were not introduced in biology until the late 1990s. Even today, there are two distinct groups: on the one hand, researchers with a background in computer science, and on the other hand, their colleagues with a background in the life sciences, with only limited interaction between the two groups. To improve this situation, the BioLINK group holds regular open meetings to bring together researchers developing text data mining tools and related language processing methods to manage the information explosion in the biomedical field. They include invited and contributed papers, with a focus on developing shared infrastructure (tools, corpora, ontologies) and challenge evaluations, in the style of the KDD Challenge Cups [2]. This year, the BioLINK SIG meeting focused on resources and tools for text mining, with special emphasis on the evaluation of these tools. Speakers from the following areas were invited:

The concept of polarity is pervasive in natural language. It relates syntax, semantics and pragmatics narrowly (Giannakidou, in: Maienborn, von Heusinger, Portner (eds.), Semantics: an international handbook of natural language meaning, De Gruyter Mouton, Berlin, 2011; Israel in The grammar of polarity: pragmatics, sensitivity, and the logic of scales, Cambridge studies in linguistics, Cambridge University Press, Cambridge, 2014), it refers to items of many syntactic categories such as nouns, verbs and adverbs. Neutral polarity items appear in affirmative and negative sentences, negative polarity items cannot appear in affirmative sentences, and positive polarity items cannot appear in negative sentences. A way of reasoning in Natural Language is through Natural Logic (van Benthem in Essays in logical semantics, vol. 29 of Studies in linguistics and philosophy, Reidel, Dordrecht, 1986; Language in action: categories, lambdas, and dynamic logic, vol. 130 of Studies in logic, Elsevier, Amsterdam, 1991). This logic is based on the concept of polarity in order to make the meaning of a sentence weaker o stronger without changing its truth value. There exist many proposals to compute polarity in the Natural Logic context, the most widely known are the ones by: van Benthem (1986, 1991), Sanchez-Valencia (Studies on natural logic and categorial grammar, Ph.D. thesis, Universiteit van Amsterdam, 1991), Dowty (Proceedings of the 4th conference on semantics and theoretical linguistics, Cornel University, CLC Publications, Rochester, 1994), and van Eijck (in: ten Cate, Zeevat (eds.), 6th international Tbilisi symposium on logic, language, and computation, Batumi, Georgia, Springer, 2007). If Natural Logic is going to be used, as an inferential mechanism between text fragments, in Natural Language Processing applications such as text summarization, question answering, and information extraction, it is a priority to know what the existing relationship among the aforementioned algorithms is; for example, to implement the most general. We show in this paper the equivalence among the analyzed algorithms, filling a gap in Natural Logic research, particularly in computing polarity, and the soundness of their algorithms.

Aiming to identify entity pairs with biomedical semantic relations and assign specific relation types, biomedical relation extraction (BioRE) plays a critical role in biomedical text mining and information extraction (IE). Recent studies indicate that general large language models (LLMs) have made some breakthroughs in general relation extraction (RE) tasks. However, even the advanced open-source LLMs struggle with BioRE tasks. For example, WizardLM-70B and LLaMA-2-70B achieve F-scores of 14.05 and 12.21 on the BioRED dataset, respectively, significantly lagging behind the state-of-the-art (SOTA) method which scores 65.17. To address this gap, a multitask instruction-tuning framework is proposed, which can transform general LLMs into BioRE-specialized models with our meticulously curated instruction dataset, REInstruct, comprising 150000 diverse and quality instruction-response pairs. Consequently, we introduce REaMA, a series of open-source LLMs with sizes of 7B and 13B specifically tailored for BioRE tasks. Experimental results on seven representative BioRE datasets show that both REaMA-2-7B and REaMA-2-13B acquire promising performance on all datasets. Remarkably, the larger REaMA-2-13B outperforms the current SOTA method on five out of seven datasets. The result exhibits the effectiveness of instruction-tuning on REInstruct in eliciting strong RE capabilities in LLMs. Furthermore, we show that incorporating chain of thought (CoT) into REInstruct can further enhance the generalization ability of REaMA. The project is available at https://github.com/stzpp/REaMA.