Relevant bibliographies by topics / Text detection and recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Text detection and recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Text detection and recognition"

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Pathak, Prakhar, Pulkit Gupta, Nishant Kishore, Nikhil Kumar Yadav, and Dr Himanshu Chaudhary. "Text Detection and Recognition: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2733–40. http://dx.doi.org/10.22214/ijraset.2022.42932.

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Abstract: In this review paper we have done extensive reading of various research paper on Text Detection and Recognition from images by different authors of around the world. Each research paper deploys different algorithms and strategies for text detection and text recognition of image. At last, we have compared the Accuracy as well as Precision and Recall Rate of the various methods used in different research paper. Keywords: Accuracy, Precision, recall rate, Digit recognition.
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BALAJI, P. "A Survey on Scene Text Detection and Text Recognition." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (March 31, 2018): 1676–84. http://dx.doi.org/10.22214/ijraset.2018.3260.

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Nazari, Narges Honarvar, Tianxiang Tan, and Yao-Yi Chiang. "Integrating Text Recognition for Overlapping Text Detection in Maps." Electronic Imaging 2016, no. 17 (February 17, 2016): 1–8. http://dx.doi.org/10.2352/issn.2470-1173.2016.17.drr-061.

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Makhmudov, Fazliddin, Mukhriddin Mukhiddinov, Akmalbek Abdusalomov, Kuldoshbay Avazov, Utkir Khamdamov, and Young Im Cho. "Improvement of the end-to-end scene text recognition method for “text-to-speech” conversion." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 06 (September 15, 2020): 2050052. http://dx.doi.org/10.1142/s0219691320500526.

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Methods for text detection and recognition in images of natural scenes have become an active research topic in computer vision and have obtained encouraging achievements over several benchmarks. In this paper, we introduce a robust yet simple pipeline that produces accurate and fast text detection and recognition for the Uzbek language in natural scene images using a fully convolutional network and the Tesseract OCR engine. First, the text detection step quickly predicts text in random orientations in full-color images with a single fully convolutional neural network, discarding redundant intermediate stages. Then, the text recognition step recognizes the Uzbek language, including both the Latin and Cyrillic alphabets, using a trained Tesseract OCR engine. Finally, the recognized text can be pronounced using the Uzbek language text-to-speech synthesizer. The proposed method was tested on the ICDAR 2013, ICDAR 2015 and MSRA-TD500 datasets, and it showed an advantage in efficiently detecting and recognizing text from natural scene images for assisting the visually impaired.
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Lin, Han, Peng Yang, and Fanlong Zhang. "Review of Scene Text Detection and Recognition." Archives of Computational Methods in Engineering 27, no. 2 (January 11, 2019): 433–54. http://dx.doi.org/10.1007/s11831-019-09315-1.

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Zhang, Fan, Jiaxing Luan, Zhichao Xu, and Wei Chen. "DetReco: Object-Text Detection and Recognition Based on Deep Neural Network." Mathematical Problems in Engineering 2020 (July 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/2365076.

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Deep learning-based object detection method has been applied in various fields, such as ITS (intelligent transportation systems) and ADS (autonomous driving systems). Meanwhile, text detection and recognition in different scenes have also attracted much attention and research effort. In this article, we propose a new object-text detection and recognition method termed “DetReco” to detect objects and texts and recognize the text contents. The proposed method is composed of object-text detection network and text recognition network. YOLOv3 is used as the algorithm for the object-text detection task and CRNN is employed to deal with the text recognition task. We combine the datasets of general objects and texts together to train the networks. At test time, the detection network detects various objects in an image. Then, the text images are passed to the text recognition network to derive the text contents. The experiments show that the proposed method achieves 78.3 mAP (mean Average Precision) for general objects and 72.8 AP (Average Precision) for texts in regard to detection performance. Furthermore, the proposed method is able to detect and recognize affine transformed or occluded texts with robustness. In addition, for the texts detected around general objects, the text contents can be used as the identifier to distinguish the object.
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Lokkondra, Chaitra Yuvaraj, Dinesh Ramegowda, Gopalakrishna Madigondanahalli Thimmaiah, and Ajay Prakash Bassappa Vijaya. "DEFUSE: Deep Fused End-to-End Video Text Detection and Recognition." Revue d'Intelligence Artificielle 36, no. 3 (June 30, 2022): 459–66. http://dx.doi.org/10.18280/ria.360314.

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Detecting and recognizing text in natural scene videos and images has brought more attention to computer vision researchers due to applications like robotic navigation and traffic sign detection. In addition, Optical Character Recognition (OCR) technology is applied to detect and recognize text on the license plate. It will be used in various commercial applications such as finding stolen cars, calculating parking fees, invoicing tolls, or controlling access to safety zones and aids in detecting fraud and secure data transactions in the banking industry. Much effort is required when scene text videos are in low contrast and motion blur with arbitrary orientations. Presently, text detection and recognition approaches are limited to static images like horizontal or approximately horizontal text. Detecting and recognizing text in videos with data dynamicity is more challenging because of the presence of multiple blurs caused by defocusing, motion, illumination changes, arbitrarily shaped, and occlusion. Thus, we proposed a combined DeepEAST (Deep Efficient and Accurate Scene Text Detector) and Keras OCR model to overcome these challenges in the proffered DEFUSE (Deep Fused) work. This two-combined technique detects the text regions and then deciphers the result into a machine-readable format. The proposed method has experimented with three different video datasets such as ICDAR 2015, Road Text 1K, and own video Datasets. Our results proved to be more effective with precision, recall, and F1-Score.
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Cahyadi, Septian, Febri Damatraseta, and Lodryck Lodefikus S. "Comparative Analysis Of Efficient Image Segmentation Technique For Text Recognition And Human Skin Recognition." Jurnal Informatika Kesatuan 1, no. 1 (July 13, 2021): 81–90. http://dx.doi.org/10.37641/jikes.v1i1.775.

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Computer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in realtime from the camera device. Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book. Meanwhile, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98% Key word: Computer Vision, Segmentation, Object Recognition, Text Recognition, Skin Color Detection, Motion Detection, Disability Application
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Li, Chunlan. "Research on Methods of English Text Detection and Recognition Based on Neural Network Detection Model." Scientific Programming 2021 (December 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/6406856.

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With the rapid development of computer science, a large number of images and an explosive amount of information make it difficult to filter and effectively extract information. This article focuses on the inability of effective detection and recognition of English text content to conduct research, which is useful for improving the application of intelligent analysis significance. This paper studies how to improve the neural network model to improve the efficiency of image text detection and recognition under complex background. The main research work is as follows: (1) An improved CTPN multidirectional text detection algorithm is proposed, and the algorithm is applied to the multidirectional text detection and recognition system. It uses the multiangle rotation of the image to be detected, then fuses the candidate text boxes detected by the CTPN network, and uses the fusion strategy to find the best area of the text. This algorithm solves the problem that the CTPN network can only detect the text in the approximate horizontal direction. (2) An improved CRNN text recognition algorithm is proposed. The algorithm is based on CRNN and combines traditional text features and depth features at the same time, making it possible to recognize occluded text. The algorithm was tested on the IC13 and SVT data sets. Compared with the CRNN algorithm, the recognition accuracy has been improved, and the detection and recognition accuracy has increased by 0.065. This paper verifies the effectiveness of the improved algorithm model on multiple data sets, which can effectively detect various English texts, and greatly improves the detection and recognition performance of the original algorithm.
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Jose, John Anthony C., Allysa Kate M. Brillantes, Elmer P. Dadios, Edwin Sybingco, Laurence A. Gan Lim, Alexis M. Fillone, and Robert Kerwin C. Billones. "Recognition of Hybrid Graphic-Text License Plates." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 4 (July 20, 2021): 416–22. http://dx.doi.org/10.20965/jaciii.2021.p0416.

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Most automatic license-plate recognition (ALPR) systems use still images and ignore the temporal information in videos. Videos provide rich temporal and motion information that should be considered during training and testing. This study focuses on creating an ALPR system that uses videos. The proposed system is comprised of detection, tracking, and recognition modules. The system achieved accuracies of 81.473% and 84.237% for license-plate detection and classification, respectively.
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Dissertations / Theses on the topic "Text detection and recognition"

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Brifkany, Jan, and Yasini Anass El. "Text Recognition in Natural Images : A study in Text Detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282935.

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In recent years, a surge in computer vision methods and solutions has been developed to solve the computer vision problem. By combining different methods from different areas of computer vision, computer scientists have been able to develop more advanced and sophisticated models to solve these problems. This report will cover two categories, text detection and text recognition. These areas will be defined, described, and analyzed in the result and discussion chapter. This report will cover an exciting and challenging topic, text recognition in natural images. It set out to assess the improvement of OCR accuracy after three image segmentation methods have been applied to images. The methods used are Maximally stable extremal regions and geometric filtering based on geometric properties. The result showed that the accuracy of OCR with segmentation methods had an overall better accuracy when compared to OCR without segmentation methods. Also, it was shown that images with horizontal text orientation had better accuracy when applying OCR with segmentation methods compared to images with multi-oriented text orientation.
Under de senaste åren har en ökning av datorseende metoder och lösningar utvecklats för att lösa datorseende problemet. Genom att kombinera olika metoder från olika områden av datorseende har datavetare kunnat utveckla mer avancerade och komplexa modeller för att lösa dessa problem. Denna rapport kommer att omfatta två kategorier, textidentifiering och textigenkänning. Dessa områden kommer att definieras, beskrivas och analyseras i resultat- och diskussionskapitlet. Denna rapport kommer att omfatta ett mycket intressant och utmanande ämne, textigenkänning i naturliga bilder. Rapporten syftar till att bedöma förbättringen av OCR-resultatet efter det att tre bildsegmenteringsmetoder har tillämpats på bilder. Metoderna som har använts är ” Maximally stable extremal regions” och geometrisk filtrering baserad på geometriska egenskaper. Resultatet visade att hos OCR med segmenteringsmetoder hade en övergripande bättre resultat jämfört med OCR utan segmenteringsmetoder. Det visades också att bilder med horisontell textorientering hade bättre noggrannhet vid tillämpning av OCR med segmenteringsmetoder jämfört med bilder med flerorienterad textorientering.
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Khiari, El Hebri. "Text Detection and Recognition in the Automotive Context." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32458.

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This thesis achieved the goal of obtaining high accuracy rates (precision and recall) in a real-time system that detects and recognizes text in the automotive context. For the sake of simplicity, this work targets two Objects of Interest (OOIs): North American (NA) traffic boards (TBs) and license plates (LPs). The proposed approach adopts a hybrid detection module consisting of a Connected Component Analysis (CCA) step followed by a Texture Analysis (TA) step. An initial set of candidates is extracted by highlighting the Maximally Stable Extremal Regions (MSERs). Each sebsequent step in the CCA and TA steps attempts to reduce the size of the set by filtering out false positives and retaining the true positives. The final set of candidates is fed into a recognition stage that integrates an open source Optical Character Reader (OCR) into the framework by using two additional steps that serve the purpose of minimizing false readings as well as the incurred delays. A set of of manually taken videos from various regions of Ottawa were used to evaluate the performance of the system, using precision, recall and latency as metrics. The high precision and recall values reflect the proposed approach's ability in removing false positives and retaining the true positives, respectively, while the low latency values deem it suitable for the automotive context. Moreover, the ability to detect two OOIs of varying appearances demonstrates the flexibility that is featured by the hybrid detection module.
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Yousfi, Sonia. "Embedded Arabic text detection and recognition in videos." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI069/document.

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Cette thèse s'intéresse à la détection et la reconnaissance du texte arabe incrusté dans les vidéos. Dans ce contexte, nous proposons différents prototypes de détection et d'OCR vidéo (Optical Character Recognition) qui sont robustes à la complexité du texte arabe (différentes échelles, tailles, polices, etc.) ainsi qu'aux différents défis liés à l'environnement vidéo et aux conditions d'acquisitions (variabilité du fond, luminosité, contraste, faible résolution, etc.). Nous introduisons différents détecteurs de texte arabe qui se basent sur l'apprentissage artificiel sans aucun prétraitement. Les détecteurs se basent sur des Réseaux de Neurones à Convolution (ConvNet) ainsi que sur des schémas de boosting pour apprendre la sélection des caractéristiques textuelles manuellement conçus. Quant à notre méthodologie d'OCR, elle se passe de la segmentation en traitant chaque image de texte en tant que séquence de caractéristiques grâce à un processus de scanning. Contrairement aux méthodes existantes qui se basent sur des caractéristiques manuellement conçues, nous proposons des représentations pertinentes apprises automatiquement à partir des données. Nous utilisons différents modèles d'apprentissage profond, regroupant des Auto-Encodeurs, des ConvNets et un modèle d'apprentissage non-supervisé, qui génèrent automatiquement ces caractéristiques. Chaque modèle résulte en un système d'OCR bien spécifique. Le processus de reconnaissance se base sur une approche connexionniste récurrente pour l'apprentissage de l'étiquetage des séquences de caractéristiques sans aucune segmentation préalable. Nos modèles d'OCR proposés sont comparés à d'autres modèles qui se basent sur des caractéristiques manuellement conçues. Nous proposons, en outre, d'intégrer des modèles de langage (LM) arabes afin d'améliorer les résultats de reconnaissance. Nous introduisons différents LMs à base des Réseaux de Neurones Récurrents capables d'apprendre des longues interdépendances linguistiques. Nous proposons un schéma de décodage conjoint qui intègre les inférences du LM en parallèle avec celles de l'OCR tout en introduisant un ensemble d’hyper-paramètres afin d'améliorer la reconnaissance et réduire le temps de réponse. Afin de surpasser le manque de corpus textuels arabes issus de contenus multimédia, nous mettons au point de nouveaux corpus manuellement annotés à partir des flux TV arabes. Le corpus conçu pour l'OCR, nommé ALIF et composée de 6,532 images de texte annotées, a été publié a des fins de recherche. Nos systèmes ont été développés et évalués sur ces corpus. L’étude des résultats a permis de valider nos approches et de montrer leurs efficacité et généricité avec plus de 97% en taux de détection, 88.63% en taux de reconnaissance mots sur le corpus ALIF dépassant ainsi un des systèmes d'OCR commerciaux les mieux connus par 36 points
This thesis focuses on Arabic embedded text detection and recognition in videos. Different approaches robust to Arabic text variability (fonts, scales, sizes, etc.) as well as to environmental and acquisition condition challenges (contrasts, degradation, complex background, etc.) are proposed. We introduce different machine learning-based solutions for robust text detection without relying on any pre-processing. The first method is based on Convolutional Neural Networks (ConvNet) while the others use a specific boosting cascade to select relevant hand-crafted text features. For the text recognition, our methodology is segmentation-free. Text images are transformed into sequences of features using a multi-scale scanning scheme. Standing out from the dominant methodology of hand-crafted features, we propose to learn relevant text representations from data using different deep learning methods, namely Deep Auto-Encoders, ConvNets and unsupervised learning models. Each one leads to a specific OCR (Optical Character Recognition) solution. Sequence labeling is performed without any prior segmentation using a recurrent connectionist learning model. Proposed solutions are compared to other methods based on non-connectionist and hand-crafted features. In addition, we propose to enhance the recognition results using Recurrent Neural Network-based language models that are able to capture long-range linguistic dependencies. Both OCR and language model probabilities are incorporated in a joint decoding scheme where additional hyper-parameters are introduced to boost recognition results and reduce the response time. Given the lack of public multimedia Arabic datasets, we propose novel annotated datasets issued from Arabic videos. The OCR dataset, called ALIF, is publicly available for research purposes. As the best of our knowledge, it is first public dataset dedicated for Arabic video OCR. Our proposed solutions were extensively evaluated. Obtained results highlight the genericity and the efficiency of our approaches, reaching a word recognition rate of 88.63% on the ALIF dataset and outperforming well-known commercial OCR engine by more than 36%
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Olsson, Oskar, and Moa Eriksson. "Automated system tests with image recognition : focused on text detection and recognition." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160249.

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Today’s airplanes and modern cars are equipped with displays to communicate important information to the pilot or driver. These displays needs to be tested for safety reasons; displays that fail can be a huge safety risk and lead to catastrophic events. Today displays are tested by checking the output signals or with the help of a person who validates the physical display manually. However this technique is very inefficient and can lead to important errors being unnoticed. MindRoad AB is searching for a solution where validation of the display is made from a camera pointed at it, text and numbers will then be recognized using a computer vision algorithm and validated in a time efficient and accurate way. This thesis compares the three different text detection algorithms, EAST, SWT and Tesseract to determine the most suitable for continued work. The chosen algorithm is then optimized and the possibility to develop a program which meets MindRoad ABs expectations is investigated. As a result several algorithms were combined to a fully working program to detect and recognize text in industrial displays.
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Chen, Datong. "Text detection and recognition in images and video sequences /." [S.l.] : [s.n.], 2003. http://library.epfl.ch/theses/?display=detail&nr=2863.

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Mešár, Marek. "Svět kolem nás jako hyperlink." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236204.

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Document describes selected techniques and approaches to problem of text detection, extraction and recognition on modern mobile devices. It also describes their proper presentation to the user interface and their conversion to hyperlinks as a source of information about surrounding world. The paper outlines text detection and recognition technique based on MSER detection and also describes the use of image features tracking method for text motion estimation.
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Fraz, Muhammad. "Video content analysis for intelligent forensics." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/18065.

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The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild.
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Wigington, Curtis Michael. "End-to-End Full-Page Handwriting Recognition." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7099.

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Despite decades of research, offline handwriting recognition (HWR) of historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives. Historical documents are plagued with noise, degradation, ink bleed-through, overlapping strokes, variation in slope and slant of the writing, and inconsistent layouts. Often the documents in a collection have been written by thousands of authors, all of whom have significantly different writing styles. In order to better capture the variations in writing styles we introduce a novel data augmentation technique. This methods achieves state-of-the-art results on modern datasets written in English and French and a historical dataset written in German.HWR models are often limited by the accuracy of the preceding steps of text detection and segmentation.Motivated by this, we present a deep learning model that jointly learns text detection, segmentation, and recognition using mostly images without detection or segmentation annotations.Our Start, Follow, Read (SFR) model is composed of a Region Proposal Network to find the start position of handwriting lines, a novel line follower network that incrementally follows and preprocesses lines of (perhaps curved) handwriting into dewarped images, and a CNN-LSTM network to read the characters. SFR exceeds the performance of the winner of the ICDAR2017 handwriting recognition competition, even when not using the provided competition region annotations.
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Jaderberg, Maxwell. "Deep learning for text spotting." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:e893c11e-6b6b-4d11-bb25-846bcef9b13e.

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This thesis addresses the problem of text spotting - being able to automatically detect and recognise text in natural images. Developing text spotting systems, systems capable of reading and therefore better interpreting the visual world, is a challenging but wildly useful task to solve. We approach this problem by drawing on the successful developments in machine learning, in particular deep learning and neural networks, to present advancements using these data-driven methods. Deep learning based models, consisting of millions of trainable parameters, require a lot of data to train effectively. To meet the requirements of these data hungry algorithms, we present two methods of automatically generating extra training data without any additional human interaction. The first crawls a photo sharing website and uses a weakly-supervised existing text spotting system to harvest new data. The second is a synthetic data generation engine, capable of generating unlimited amounts of realistic looking text images, that can be solely relied upon for training text recognition models. While we define these new datasets, all our methods are also evaluated on standard public benchmark datasets. We develop two approaches to text spotting: character-centric and word-centric. In the character-centric approach, multiple character classifier models are developed, reinforcing each other through a feature sharing framework. These character models are used to generate text saliency maps to drive detection, and convolved with detection regions to enable text recognition, producing an end-to-end system with state-of-the-art performance. For the second, higher-level, word-centric approach to text spotting, weak detection models are constructed to find potential instances of words in images, which are subsequently refined and adjusted with a classifier and deep coordinate regressor. A whole word image recognition model recognises words from a huge dictionary of 90k words using classification, resulting in previously unattainable levels of accuracy. The resulting end-to-end text spotting pipeline advances the state of the art significantly and is applied to large scale video search. While dictionary based text recognition is useful and powerful, the need for unconstrained text recognition still prevails. We develop a two-part model for text recognition, with the complementary parts combined in a graphical model and trained using a structured output learning framework adapted to deep learning. The trained recognition model is capable of accurately recognising unseen and completely random text. Finally, we make a general contribution to improve the efficiency of convolutional neural networks. Our low-rank approximation schemes can be utilised to greatly reduce the number of computations required for inference. These are applied to various existing models, resulting in real-world speedups with negligible loss in predictive power.
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Lu, Hsin-Min. "SURVEILLANCE IN THE INFORMATION AGE: TEXT QUANTIFICATION, ANOMALY DETECTION, AND EMPIRICAL EVALUATION." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/193893.

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Deep penetration of personal computers, data communication networks, and the Internet has created a massive platform for data collection, dissemination, storage, and retrieval. Large amounts of textual data are now available at a very low cost. Valuable information, such as consumer preferences, new product developments, trends, and opportunities, can be found in this large collection of textual data. Growing worldwide competition, new technology development, and the Internet contribute to an increasingly turbulent business environment. Conducting surveillance on this growing collection of textual data could help a business avoid surprises, identify threats and opportunities, and gain competitive advantages.Current text mining approaches, nonetheless, provide limited support for conducting surveillance using textual data. In this dissertation, I develop novel text quantification approaches to identify useful information in textual data, effective anomaly detection approaches to monitor time series data aggregated based on the text quantification approaches, and empirical evaluation approaches that verify the effectiveness of text mining approaches using external numerical data sources.In Chapter 2, I present free-text chief complaint classification studies that aim to classify incoming emergency department free-text chief complaints into syndromic categories, a higher level of representation that facilitates syndromic surveillance. Chapter 3 presents a novel detection algorithm based on Markov switching with jumps models. This surveillance model aims at detecting different types of disease outbreaks based on the time series generated from the chief complaint classification system.In Chapters 4 and 5, I studied the surveillance issue under the context of business decision making. Chapter 4 presents a novel text-based risk recognition design framework that can be used to monitor the changing business environment. Chapter 5 presents an empirical evaluation study that looks at the interaction between news sentiment and numerical accounting earnings information. Chapter 6 concludes this dissertation by highlighting major research contributions and the relevance to MIS research.
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Books on the topic "Text detection and recognition"

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Chen, Datong. Text detection and recognition in images and video sequences. Lausanne: EPFL, 2003.

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Rajalingam, Mallikka. Text Segmentation and Recognition for Enhanced Image Spam Detection. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53047-1.

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Saleeb, Atef F. Defect localization capabilities of a global detection scheme: Spatial pattern recognition using full-field vibration test data in plates. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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Lu, Tong, Shivakumara Palaiahnakote, Chew Lim Tan, and Wenyin Liu. Video Text Detection. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6515-6.

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Cipolla, Roberto, Sebastiano Battiato, and Giovanni Maria Farinella. Computer vision: Detection, recognition and reconstruction. Berlin: Springer, 2010.

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Drugs and the law: Detection, recognition & investigation. [Altamonte Springs, FL]: Gould Publications, 1992.

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Bogusław Cyganek. Object Detection and Recognition in Digital Images. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118618387.

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Jiang, Xiaoyue, Abdenour Hadid, Yanwei Pang, Eric Granger, and Xiaoyi Feng, eds. Deep Learning in Object Detection and Recognition. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4.

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Drugs and the law: Detection, recognition & investigation. Charlottesville, VA: LexisNexis, 2014.

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1950-, Srihari Sargur N., ed. Computer text recognition and error correction. Silver Spring, MD: IEEE Computer Society Press, 1985.

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Book chapters on the topic "Text detection and recognition"

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Lu, Tong, Shivakumara Palaiahnakote, Chew Lim Tan, and Wenyin Liu. "Character Segmentation and Recognition." In Video Text Detection, 145–68. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6515-6_6.

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Huang, Huijuan, Zhi Tian, Tong He, Weilin Huang, and Yu Qiao. "Orientation-Aware Text Proposals Network for Scene Text Detection." In Biometric Recognition, 739–49. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_79.

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Rajalingam, Mallikka. "Character Recognition." In Text Segmentation and Recognition for Enhanced Image Spam Detection, 71–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53047-1_5.

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Zhang, Wenqing, Yang Qiu, Minghui Liao, Rui Zhang, Xiaolin Wei, and Xiang Bai. "Scene Text Detection with Scribble Line." In Document Analysis and Recognition – ICDAR 2021, 79–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86337-1_6.

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Sheng, Tao, and Zhouhui Lian. "Bidirectional Regression for Arbitrary-Shaped Text Detection." In Document Analysis and Recognition – ICDAR 2021, 187–201. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86337-1_13.

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Mihelič, France, and Janez Žibert. "Robust Speech Detection Based on Phoneme Recognition Features." In Text, Speech and Dialogue, 455–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11846406_57.

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Wang, Hsueh-Cheng, Yafim Landa, Maurice Fallon, and Seth Teller. "Spatially Prioritized and Persistent Text Detection and Decoding." In Camera-Based Document Analysis and Recognition, 3–17. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05167-3_1.

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George, Sonia, and Noopa Jagadeesh. "Robust Text Detection and Recognition in Blurred Images." In Proceedings of the International Conference on Soft Computing Systems, 125–34. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2671-0_12.

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Darshan, H. Y., M. T. Gopalkrishna, and M. C. Hanumantharaju. "Text Detection and Recognition Using Camera Based Images." In Advances in Intelligent Systems and Computing, 573–79. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12012-6_63.

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Kataria, Mahima, Prashansa Gupta, Shivani Singh, Vani Bansal, and M. Ravinder. "Review on text detection and recognition in images." In Artificial Intelligence and Speech Technology, 355–61. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003150664-39.

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Conference papers on the topic "Text detection and recognition"

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Hu, ZiLing, Xingiiao Wu, and Jing Yang. "TCATD: Text Contour Attention for Scene Text Detection." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412223.

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Ilyasi, Pervez Shoaib, Gautam Gupta, M. Sravan Sai, K. Saatwik, B. Shiva Kumar, and Dinesh Vij. "Object-Text Detection and Recognition System." In 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2021. http://dx.doi.org/10.1109/smart52563.2021.9675310.

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Pan, Runqiu, Zezhou Li, and Anna Zhu. "Find More Accurate Text Boundary for Scene Text Detection." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956596.

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Xiaoming Huang, Tao Shen, Run Wang, and Chenqiang Gao. "Text detection and recognition in natural scene images." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280160.

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Zhu, Xiangyu, Yingying Jiang, Shuli Yang, Xiaobing Wang, Wei Li, Pei Fu, Hua Wang, and Zhenbo Luo. "Deep Residual Text Detection Network for Scene Text." In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017. http://dx.doi.org/10.1109/icdar.2017.137.

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Qin, Longfei, Palaiahnakote Shivakumara, Tong Lu, Umapada Pal, and Chew Lim Tan. "Video scene text frames categorization for text detection and recognition." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900241.

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Dai, Yuchen, Zheng Huang, Yuting Gao, Youxuan Xu, Kai Chen, Jie Guo, and Weidong Qiu. "Fused Text Segmentation Networks for Multi-oriented Scene Text Detection." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8546066.

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Zongyi Liu and Sudeep Sarkar. "Robust outdoor text detection using text intensity and shape features." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761432.

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Hu, Ping, Weiqiang Wang, and Ke Lu. "Video text detection with text edges and convolutional neural network." In 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2015. http://dx.doi.org/10.1109/acpr.2015.7486588.

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Xylogiannopoulos, Konstantinos, Panagiotis Karampelas, and Reda Alhajj. "Text Mining for Plagiarism Detection: Multivariate Pattern Detection for Recognition of Text Similarities." In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2018. http://dx.doi.org/10.1109/asonam.2018.8508265.

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Reports on the topic "Text detection and recognition"

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Godil, Afzal, Patrick Grother, and Mei Ngan. The text recognition algorithm independent evaluation (TRAIT). Gaithersburg, MD: National Institute of Standards and Technology, December 2017. http://dx.doi.org/10.6028/nist.ir.8199.

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Seo, Young-Woo, and Katia Sycara. Text Clustering for Topic Detection. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada599196.

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Mouroulis, P. Visual target detection and recognition. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/5087944.

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Karakowski, Joseph A., and Hai H. Phu. Text Independent Speaker Recognition Using A Fuzzy Hypercube Classifier. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada354792.

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Grenander, Ulf. Foundations of Object Detection and Recognition,. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada352287.

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Li, Huiping, David Doermann, and Omid Kia. Automatic Text Detection and Tracking in Digital Video. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada458675.

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Dittmar, George. Object Detection and Recognition in Natural Settings. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.926.

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Chun, Cornell S., and Firooz A. Sadjadi. Polarimetric Imaging System for Automatic Target Detection and Recognition. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada395219.

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Devaney, A. J., R. Raghavan, H. Lev-Ari, E. Manolakos, and M. Kokar. Automatic Target Detection And Recognition: A Wavelet Based Approach. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada329696.

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Hupp, N. A. Detection of Prosodics by Using a Speech Recognition System. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada242432.

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