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: 25 July 2025

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Journal articles on the topic "Text detection and recognition"

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Zheng, Qihang, and Yaping Zhang. "Text Detection and Recognition for X-ray Weld Seam Images." Applied Sciences 14, no. 6 (2024): 2422. http://dx.doi.org/10.3390/app14062422.

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X-ray weld seam images carry vital information about welds. Leveraging graphic–text recognition technology enables intelligent data collection in complex industrial environments, promising significant improvements in work efficiency. This study focuses on using deep learning methods to enhance the accuracy and efficiency of detecting weld seam information. We began by actively gathering a dataset of X-ray weld seam images for model training and evaluation. The study comprises two main components: text detection and text recognition. For text detection, we employed a model based on the DBNet al
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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 (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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CSE, Manish Kushwaha. "Text Detection And Recognition: A Review." IOSR Journal of Computer Engineering 26, no. 5 (2024): 36–41. http://dx.doi.org/10.9790/0661-2605033641.

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This paper identifies and compares different stages in the process of text detection and recognition and analyses different approaches used for text extraction from color images. Two commonly used methods for this problem are stepwise methods and integrated methods, whereas this task is further divided into text detection and localization, classification, segmentation and text recognition. Important approaches used to undergo these stages and their corresponding advantages, disadvantages and applications are presented in this paper. Various text related applications for imagery are also presen
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Xiang, Liuqing, Hanyun Wen, and Ming Zhao. "Pill Box Text Identification Using DBNet-CRNN." International Journal of Environmental Research and Public Health 20, no. 5 (2023): 3881. http://dx.doi.org/10.3390/ijerph20053881.

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The recognition process of natural scenes is complicated at present, and images themselves may be complex owing to the special features of natural scenes. In this study, we use the detection and recognition of pill box text as an application scenario and design a deep-learning-based text detection algorithm for such natural scenes. We propose an end-to-end graphical text detection and recognition model and implement a detection system based on the B/S research application for pill box recognition, which uses DBNet as the text detection framework and a convolutional recurrent neural network (CR
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Shetty, Ashik N. "A Unified Flask-Based Framework for Image Text Recognition, Multilingual Translation, and Text Summarization." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 4759–63. https://doi.org/10.22214/ijraset.2025.69051.

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This study presents a comprehensive review of OCR (optical character recognition), Translation, and Object Detection Research from a single image. With the fast advancement of deep learning, more powerful tools that can learn semantic, highlevel, and deeper features have been proposed to solve the issues that plague traditional systems. The rise of high-powered desktop computer has aided OCR reading technology by permitting the creation of more sophisticated recognition software that can read a range of common printed typefaces and handwritten texts. However, implementing an OCR that works in
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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 (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 (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 (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 inte
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P, Golda Jeyasheeli, Athinarayanan B, Manish T, and Mohamad Umar M. "Scene Text Detection and Recognition Using Maximally Stable Extremal Region." Journal of Applied Engineering and Technological Science (JAETS) 6, no. 1 (2024): 103–14. https://doi.org/10.37385/jaets.v6i1.5958.

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In recent years, scene text detection and recognition have become important research areas in computer vision and machine learning. Traditional text detection and recognition methods may struggle with detecting and recognizing text in images with low resolution, complex backgrounds, and varying font sizes. The proposed methodology addresses these challenges by combining multiple algorithms and using deep learning techniques. In this paper, we propose a method for scene text detection based on Maximally Stable Extremal Regions (MSER) combined with Stroke Width Transform (SWT) and recognition us
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Kiptanui, Linus, J. Prabhakar C, and R. Shrinivasa S. "Rectification of Curved Scene Text Based on B-Spline Curve Fitting." Indian Journal of Science and Technology 17, no. 32 (2024): 3305–17. https://doi.org/10.17485/IJST/v17i32.2402.

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Abstract <strong>Objectives:</strong>&nbsp;In this study, we proposed suitable technique for rectification of curved scene text which is followed by recognition of rectified text in order to improve the accuracy of the existing techniques.&nbsp;<strong>Methods:</strong>&nbsp;In order to rectify curved text, initially, we perform curved text detection using Look More Than Twice (LOMT) model which detects and locates curved text. The detected text area is binarized through adaptive binarizaton technique. Then, we rectify the detected curved text through B-spline based curve fitting which align t
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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 improveme
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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 numb
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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 th
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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.
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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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Wang, Kewei. "Multilingual text-image recognition based on zero real sample learning." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29579.

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Scene text recognition (STR), derived from optical character recognition (OCR), has been extensively studied and made marvelous achievements in the past decades. While great progress has been made in majority languages such as Chinese and English, however, for most of the minority languages, the exceptional lack of annotated text databases for training purposes is always exists. Thus, the paper aims to enhance the overall performance of multilingual STR models for minority languages. We strictly choose Japanese as a target minority language and build a novel STR model. For text detection, w
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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
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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 nove
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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 effectivel
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Books on the topic "Text detection and recognition"

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

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

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Center, NASA Glenn Research, ed. Defect localization capabilities of a global detection scheme: Spatial pattern recognition using full-field vibration test data in plates. National Aeronautics and Space Administration, Glenn Research Center, 2002.

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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. 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. 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. Springer, 2010.

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Peng, Kuan-Chuan, Yizhou Wang, Ziyue Li, et al., eds. Human Activity Recognition and Anomaly Detection. Springer Nature Singapore, 2025. http://dx.doi.org/10.1007/978-981-97-9003-6.

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Yin, Xu-Cheng, Chun Yang, and Chang Liu. Open-Set Text Recognition. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0361-6.

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Bogusław Cyganek. Object Detection and Recognition in Digital Images. 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. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4.

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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. 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. 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. 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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86337-1_6.

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Sun, Xurui, Jiahao Lyu, Yifei Zhang, et al. "Feature Enhancement with Text-Specific Region Contrast for Scene Text Detection." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8540-1_1.

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Sheng, Tao, and Zhouhui Lian. "Bidirectional Regression for Arbitrary-Shaped Text Detection." In Document Analysis and Recognition – ICDAR 2021. 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. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11846406_57.

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Kumar, Shiva, P. Vignesh Prabhu, Manoj S. Bhat, Sampath Kumar, and B. Shubha. "Text Detection and Recognition Using Machine Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5866-1_28.

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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. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05167-3_1.

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Wu, Hao, Jun Zhou, Qiong Zhang, et al. "A Quantum-Based Attention Mechanism in Scene Text Detection." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8543-2_1.

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Conference papers on the topic "Text detection and recognition"

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Liang, Min, Jia-Wei Ma, Xiaobin Zhu, Jingyan Qin, and Xu-Cheng Yin. "LayoutFormer: Hierarchical Text Detection Towards Scene Text Understanding." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01483.

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Hendra, Jayson Mikael, Peter Nelson Subrata, Nunung Nurul Qomariyah, and Veronica Lestari Jauw. "Evaluating the Performance of Different Text Detection and Recognition Models for Tyre Text." In 2024 International Conference on ICT for Smart Society (ICISS). IEEE, 2024. http://dx.doi.org/10.1109/iciss62896.2024.10751018.

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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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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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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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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, et al. "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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Abass, Eman S., Abd-Elnaser Fawzy Mohamed, Ayman Amer, Mohamed Hafez, Ahmed Solyman, and Mohamed Fawzy. "Currency Recognition Using EAST for Text Detection and Tesseract OCR for Text Recognition." In 2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI). IEEE, 2023. http://dx.doi.org/10.1109/eiceeai60672.2023.10590444.

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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, et al. "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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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). National Institute of Standards and Technology, 2017. http://dx.doi.org/10.6028/nist.ir.8199.

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Andresen, Jens-Bjørn R., and Søren M. Kristiansen. Historic maps as source for hydrological reconstruction of pre-industrial landscape wetness in Denmark: a methodological study. Det Kgl. Bibliotek, 2023. http://dx.doi.org/10.7146/aul.491.

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Historic maps are an important primary source which can be utilized in the reconstruction of environmental variables of the pre-industrial landscape. However, methodological constraints have hitherto prevented large scale and systematic approaches. In this paper a novel methodology is presented, which documents the usefulness of the maps in the study of paleo-hydrology and thus serves a better understanding of the conditions for agricultural production under pre-drainage conditions. The methodology is developed based on eighteenth and nineteenth century maps from a 100 km2 study area in one st
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Seo, Young-Woo, and Katia Sycara. Text Clustering for Topic Detection. Defense Technical Information Center, 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), 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. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada354792.

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

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Griffiths, Rachael M. Handwritten Text Recognition (HTR) for Tibetan Manuscripts in Cursive Script. Verlag der Österreichischen Akademie der Wissenschaften, 2024. http://dx.doi.org/10.1553/tibschol_erc_htr.

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The use of advanced computational methods for the analysis of digitised texts is becoming increasingly popular in humanities and social science research. One such technology is Handwritten Text Recognition (HTR), which generates transcripts from digitised texts with machine learning approaches, to enable full-text search and analysis. Up to now, HTR models for Tibetan manuscripts in cursive script have not been available. This paper introduces work carried out as part of the The Dawn of Tibetan Buddhist Scholasticism (11th-13th) TibSchol) project at the Austrian Academy of Sciences, which is u
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Li, Huiping, David Doermann, and Omid Kia. Automatic Text Detection and Tracking in Digital Video. Defense Technical Information Center, 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, 2000. http://dx.doi.org/10.15760/etd.926.

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Zhao, Ruyin. CSI-based Gesture Recognition and Object Detection. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-456.

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