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Journal articles on the topic 'Text detection and recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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8

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> 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. <strong>Methods:</strong> 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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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 t

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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 (2019): 433–54. http://dx.doi.org/10.1007/s11831-019-09315-1.

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13

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 multidi

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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 (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,

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Miss., Sonali V. Gunjal, and (Guide)* Prof.N.B.Kadu. "SIMBIO: A EFFECTIVE APPROACH FOR SIMPLIFYING AGGREGATE MENTIONS IN BIOMEDICAL TEXT." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 30–35. https://doi.org/10.5281/zenodo.48822.

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One of the major challenge in biomedical named entity recognition (NER) and normalization process is the detection and decision of aggregate(compound)  named entities, in which a single entity refers to many concept e.g., SMAD/1/2. Previous research regarding named entity recognition and normalization, some of them have neglected aggregate mentions, apply simply rules for detecting, or perform coordination ellipsis, so that force to require a such method that can easily handle the different types of aggregate mentions. In this paper, we propose a new approach that combines a machine-learn

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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 (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

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Jose, John Anthony C., Allysa Kate M. Brillantes, Elmer P. Dadios, et al. "Recognition of Hybrid Graphic-Text License Plates." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 4 (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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Francisca, O. Nwokoma, N. Odii Juliet, I. Ayogu Ikechukwu, and C. Ogbonna James. "Camera-based OCR scene text detection issues: A review." World Journal of Advanced Research and Reviews 12, no. 3 (2021): 484–89. https://doi.org/10.5281/zenodo.5813901.

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Camera-based scene text detection and recognition is a research area that has attracted countless attention and had made noticeable progress in the area of deep learning technology, computer vision, and pattern recognition. They are highly recommended for capturing text on-scene images (signboards), documents with a multipart and complex background, images on thick books and documents that are highly fragile. This technology encourages real-time processing since handheld cameras are built with very high processing speed and internal memory, are quite easy and flexible to use than the tradition

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Zhang, Yanan, Zhen Guo, and Tao Sun. "A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning." Electronics 12, no. 13 (2023): 2873. http://dx.doi.org/10.3390/electronics12132873.

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In the non-intrusive automated testing system for Internet of Vehicles (IoV) applications, automatic recognition of text and icons on vehicle central control screens is of paramount importance. However, the detection and recognition of content on vehicle central control screens are inherently complex. Additionally, during non-intrusive vehicle central control screen image testing, there is a deficiency of suitable datasets and detection methods. This deficiency renders information within vehicle application images difficult to be accurately extracted by the detection network. To address this p

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., Shivani, and Dipti Bansal. "Techniques of Text Detection and Recognition: A Survey." International Journal of Emerging Research in Management and Technology 6, no. 6 (2018): 83. http://dx.doi.org/10.23956/ijermt.v6i6.250.

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The pattern recognition is the technique which is applied on the image to detect similar type of patterns from the image. The text detection and recognition are the techniques of patterns detection. To detect text area in the image techniques of image segmentation is required which will segment the area in which text is present. To mark the text from the image technique of neural networks is required which will learn from the previous values and drive new values on the basis of current network situations. In this paper, various techniques of image segmentation and neural networks has been revi

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Gomathi, Harsh, and Shubham Bhatt. "SIGN LANGUAGE DETECTION AND TEXT TO SPEECH GENERATION." International Journal of Engineering Applied Sciences and Technology 08, no. 07 (2023): 51–55. http://dx.doi.org/10.33564/ijeast.2023.v08i07.009.

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A vital technique for bridging the communication gap between hearing-impaired and normal individuals is sign language. But given the diversity of today's approximately 7000 sign languages, which vary in hand shapes, body part positions, and motion positions, automated sign language recognition (ASLR) is a challenging method. Researchers are looking into more effective ways to build ASLR systems to find intelligent solutions in order to get around this complexity, and they have shown impressive results. This purpose of this work is to examine the literature on intelligent systems for sign langu

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22

Linus, Kiptanui, C. J. Prabhakar, and S. R. Shrinivasa. "Rectification of Curved Scene Text Based on B-Spline Curve Fitting." Indian Journal Of Science And Technology 17, no. 32 (2024): 3305–17. http://dx.doi.org/10.17485/ijst/v17i32.2402.

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Objectives: 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. Methods: 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 the curved text into straight line. The rectified text is f

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23

Wang, Jieming, and Wei Wu. "Research on Natural Scene Text Detection and Recognition." Journal of Physics: Conference Series 1754, no. 1 (2021): 012200. http://dx.doi.org/10.1088/1742-6596/1754/1/012200.

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Garg, Sheetal, Akshatha P. S., and Kavyashree C. "An Extensive Survey on Text Detection and Recognition." International Journal of Computer Sciences and Engineering 7, no. 1 (2019): 546–51. http://dx.doi.org/10.26438/ijcse/v7i1.546551.

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BAI, Xiang, Minghui LIAO, Baoguang SHI, and Mingkun YANG. "Deep learning for scene text detection and recognition." SCIENTIA SINICA Informationis 48, no. 5 (2018): 531–44. http://dx.doi.org/10.1360/n112018-00003.

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Ye, Qixiang, and David Doermann. "Text Detection and Recognition in Imagery: A Survey." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 7 (2015): 1480–500. http://dx.doi.org/10.1109/tpami.2014.2366765.

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Qiao, Liang, Sanli Tang, Zhanzhan Cheng, et al. "Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11899–907. http://dx.doi.org/10.1609/aaai.v34i07.6864.

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Many approaches have recently been proposed to detect irregular scene text and achieved promising results. However, their localization results may not well satisfy the following text recognition part mainly because of two reasons: 1) recognizing arbitrary shaped text is still a challenging task, and 2) prevalent non-trainable pipeline strategies between text detection and text recognition will lead to suboptimal performances. To handle this incompatibility problem, in this paper we propose an end-to-end trainable text spotting approach named Text Perceptron. Concretely, Text Perceptron first e

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Murugan, S., and R. Karthika. "A Survey on Traffic Sign Detection Techniques Using Text Mining." Asian Journal of Computer Science and Technology 8, S1 (2019): 21–24. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1975.

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Traffic Sign Detection and Recognition (TSDR) technique is a critical step for ensuring vehicle safety. This paper provides a comprehensive survey on traffic sign detection and recognition system based on image and video data. The main focus is to present the current trends and challenges in the field of developing an efficient TSDR system. The ultimate aim of this survey is to analyze the various techniques for detecting traffic signs in real time applications. Image processing is a prominent research area, where multiple technologies are associated to convert an image into digital form and p

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Zeng, Yue, and Cai Meng. "HubNet: An E2E Model for Wheel Hub Text Detection and Recognition Using Global and Local Features." Sensors 24, no. 19 (2024): 6183. http://dx.doi.org/10.3390/s24196183.

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Automatic detection and recognition of wheel hub text, which can boost the efficiency and accuracy of product information recording, are undermined by the obscurity and orientation variability of text on wheel hubs. To address these issues, this paper constructs a wheel hub text dataset and proposes a wheel hub text detection and recognition model called HubNet. The dataset captured images on real industrial production line scenes, including 446 images, 934 word instances, and 2947 character instances. HubNet is an end-to-end text detection and recognition model, not only comprising convention

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Krishna, Mr S. Rama. "Text Detection And Extraction Using OpenCV and OCR." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–6. https://doi.org/10.55041/isjem02327.

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This is an image text detection and extraction project with OpenCV and Optical Character Recognition (OCR) methods. Preprocessing of images is performed with OpenCV, such as grayscale, noise removal, resizing, and thresholding, to improve the quality of images. Tesseract OCR is utilized to detect and extract text and convert it into a machine-readable text. The system is also able to automatically identify Regions of Interest (ROIs) in which text is likely to reside, thus streamlining text recognition. It renders it appropriate for document digitization, text extraction from signs or license p

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Li, Jun, Junwei Dai, Jia Kang, and Wei Wei. "Automatic Assembly Inspection of Satellite Payload Module Based on Text Detection and Recognition." Electronics 14, no. 12 (2025): 2423. https://doi.org/10.3390/electronics14122423.

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The payload module of a high-throughput satellite involves the complex assembly of various components, which plays a vital role in maintaining the satellite’s structural and functional integrity. To support this, inspections during the assembly process are essential for minimizing human error, reducing inspection time, and ensuring adherence to design specifications. However, the current inspection process is entirely manual. It requires substantial manpower and time and is prone to errors such as missed or false detections, which compromise the overall effectiveness of the inspection process.

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Huang, Baohua, Aokun Bai, Yuqiong Wu, Chanjuan Yang, and Han Sun. "DB-EAC and LSTR: DBnet based seal text detection and Lightweight Seal Text Recognition." PLOS ONE 19, no. 5 (2024): e0301862. http://dx.doi.org/10.1371/journal.pone.0301862.

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Recognition of the key text of the Chinese seal can speed up the approval of documents, and improve the office efficiency of enterprises or government administrative departments. Due to image blurring and occlusion, the accuracy of Chinese seal recognition is low. In addition, the real dataset is very limited. In order to solve these problems, we improve the differentiable binarization detection algorithm (DBnet) to construct a model DB-ECA for text region detection, and propose a model named LSTR (Lightweight Seal Text Recognition) for text recognition. The efficient channel attention module

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Rane, Gayatri M. "Handwritten Text Recognition and Plagiarism Detection Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50952.

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This review paper evaluates a recent study on a dual-purpose system for handwritten text recognition (HTR) and plagiarism detection, leveraging the Connectionist Temporal Classification (CTC) algorithm and cosine similarity. The reviewed work focuses on digitizing handwritten documents and ensuring content originality, with applications in academic and professional settings. This paper synthesizes the study's contributions, methodologies, and results, while situating it within the broader literature on HTR and plagiarism detection. Key insights include the system's high accuracy in recognizing

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Revathi, Buddaraju, M. V. D. Prasad, and Naveen Kishore Gattim. "Computationally efficient ResNet based Telugu handwritten text detection." Bulletin of Electrical Engineering and Informatics 13, no. 6 (2024): 4115–23. http://dx.doi.org/10.11591/eei.v13i6.8170.

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Optical character recognition (OCR) is a technological process that converts diverse document formats into editable and searchable data. Recognition of Telugu characters through OCR poses a challenge because of compound characters. Identifying handwritten Telugu text proves difficult due to the substantial number of characters, their similarities, and overlapping forms. To handle overlapping characters, we implemented a segmentation algorithm that efficiently separates these characters, consequently enhancing the model’s accuracy. Feature extraction is a crucial phase in recognizing a broader

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Zou, Yi Bo, and Yi Min Chen. "Research of Text Recognition Based on Natural Scene." Applied Mechanics and Materials 599-601 (August 2014): 1621–24. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1621.

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According to the demands of the text recognition in natural scenes, we focus on the text recognition system and its algorithm. Firstly, we present a secondary detection method in which we locate the text area quickly by using the edge of these characters and secondly introduce an improved segmentation algorithm of active contour to detect the location of the target character accurately, based on prior knowledge. Lastly, in the state of text recognition, we propose a KNN (K-Nearest Neighbor) classification method using the descriptor of shape context. The experiments show that our algorithm cou

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Gupta, Monica, Alka Choudhary, and Jyotsna Parmar. "Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition." International Journal of Computer Vision and Image Processing 11, no. 4 (2021): 39–62. http://dx.doi.org/10.4018/ijcvip.2021100104.

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In today's era, data in digitalized form is needed for faster processing and performing of all tasks. The best way to digitalize the documents is by extracting the text from them. This work of text extraction can be performed by various text identification tasks such as scene text recognition, optical character recognition, handwriting recognition, and much more. This paper presents, reviews, and analyses recent research expansion in the area of optical character recognition and scene text recognition based on various existing models such as convolutional neural network, long short-term memory

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Yang, Li, Ying Li, Jin Wang, and Zhuo Tang. "Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF." Electronics 8, no. 11 (2019): 1248. http://dx.doi.org/10.3390/electronics8111248.

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With the rapid development of Internet of Things Technology, speech recognition has been applied more and more widely. Chinese Speech Recognition is a complex process. In the process of speech-to-text conversion, due to the influence of dialect, environmental noise, and context, the accuracy of speech-to-text in multi-round dialogues and specific contexts is still not high. After the general speech recognition technology, the text after speech recognition can be detected and corrected in the specific context, which is helpful to improve the robustness of text comprehension and is a beneficial

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Maddineni, Bhavyasri. "Various Models for the Conversion of Handwritten Text to Digital Text." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2894–99. http://dx.doi.org/10.22214/ijraset.2021.35616.

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Handwritten Text Recognition (HTR) also known as Handwriting Recognition (HWR) is the detection and interpretation of handwritten text images by the computer. Handwritten text from various sources such as notebooks, documents, forms, photographs, and other devices can be given to the computer to predict and convert into the Computerized Text/Digital Text. Humans find easier to write on a piece of paper rather than typing, but now-a-days everything is being digitalized. So, HTR/HWR has an increasing use these days. There are various techniques used in recognizing the handwriting. Some of the tr

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De, Soumya, R. Joe Stanley, Beibei Cheng, Sameer Antani, Rodney Long, and George Thoma. "Automated Text Detection and Recognition in Annotated Biomedical Publication Images." International Journal of Healthcare Information Systems and Informatics 9, no. 2 (2014): 34–63. http://dx.doi.org/10.4018/ijhisi.2014040103.

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Images in biomedical publications often convey important information related to an article's content. When referenced properly, these images aid in clinical decision support. Annotations such as text labels and symbols, as provided by medical experts, are used to highlight regions of interest within the images. These annotations, if extracted automatically, could be used in conjunction with either the image caption text or the image citations (mentions) in the articles to improve biomedical information retrieval. In the current study, automatic detection and recognition of text labels in biome

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Kumar Dasari, Sunil, and Shilpa Mehta. "Text detection and recognition through deep learning-based fusion neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1396. http://dx.doi.org/10.11591/ijai.v12.i3.pp1396-1406.

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<p>Text recognition task involves recognizing the text from the natural image; it possesses various application, which aids information extraction through data mining from street view like images. Scene text recognition involves two stages i.e., text detection and text recognition, in the past several mechanisms has been proposed for accurate identification, these mechanisms are either traditional approach or deep learning-based. All the existing deep-learning methodology fails as this comprises character data and image data, further this research develops an optimal architecture fusion

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Sunil, Kumar Dasari, and Mehta Shilpa. "Text detection and recognition through deep learning-based fusion neural network." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1396–406. https://doi.org/10.11591/ijai.v12.i3.pp1396-1406.

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Text recognition task involves recognizing the text from the natural image; it possesses various application, which aids information extraction through data mining from street view like images. Scene text recognition involves two stages i.e., text detection and text recognition, in the past several mechanisms has been proposed for accurate identification, these mechanisms are either traditional approach or deep learning-based. All the existing deep-learning methodology fails as this comprises character data and image data, further this research develops an optimal architecture fusion neural ne

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Lu, Manhuai, Yi Leng, Chin-Ling Chen, and Qiting Tang. "An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection." Applied Sciences 12, no. 23 (2022): 12120. http://dx.doi.org/10.3390/app122312120.

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The street sign text information from natural scenes usually exists in a complex background environment and is affected by natural light and artificial light. However, most of the current text detection algorithms do not effectively reduce the influence of light and do not make full use of the relationship between high-level semantic information and contextual semantic information in the feature extraction network when extracting features from images, and they are ineffective at detecting text in complex backgrounds. To solve these problems, we first propose a multi-channel MSER (Maximally Sta

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Zhao, Qingyang. "Researches advanced in Natural Scenes Text Detection Based on Deep Learning." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 188–97. http://dx.doi.org/10.54097/hset.v16i.2500.

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The research on text detection and recognition in natural scenes is of great significance for obtaining information from scenes. Thanks to the rapid development of convolutional neural networks and the continuous proposal of scene text detection methods based on deep learning, breakthroughs have been made in the recognition accuracy and speed of scene texts. This paper mainly sorts, analyzes and summarizes the scene text detection method based on deep learning and its development. Firstly, the related research background and significance of scene text detection are discussed. Then, the second

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Li, Min, Liping Zhang, Mingle Zhou, and Delong Han. "UTTSR: A Novel Non-Structured Text Table Recognition Model Powered by Deep Learning Technology." Applied Sciences 13, no. 13 (2023): 7556. http://dx.doi.org/10.3390/app13137556.

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To prevent the compilation of documents, many table documents are formatted with non-editable and non-structured texts such as PDFs or images. Quickly recognizing the contents of tables is still a challenge due to factors such as irregular formats, uneven text quality, and complex and diverse table content. This article proposes the UTTSR table recognition model, which consists of four parts: text region detection, text line detection and recognition, and table sequence recognition. For table detection, the Cascade Faster RCNN with the ResNeXt105 network is implemented, using TPS (Thin Plate S

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Salam, Shaikh Abdul, and Rajkumar Gupta. "Emotion Detection and Recognition from Text using Machine Learning." International Journal of Computer Sciences and Engineering 6, no. 6 (2018): 341–45. http://dx.doi.org/10.26438/ijcse/v6i6.341345.

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Arundel, Samantha T., Trenton P. Morgan, and Dennis P. Powon. "Improving map text detection and recognition through data synthesis." Abstracts of the ICA 3 (December 13, 2021): 1–2. http://dx.doi.org/10.5194/ica-abs-3-12-2021.

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Yao, Cong, Xiang Bai, and Wenyu Liu. "A Unified Framework for Multioriented Text Detection and Recognition." IEEE Transactions on Image Processing 23, no. 11 (2014): 4737–49. http://dx.doi.org/10.1109/tip.2014.2353813.

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Liu, Chongyu, Xiaoxue Chen, Canjie Luo, Lianwen Jin, Yang Xue, and Yuliang Liu. "Deep learning methods for scene text detection and recognition." Journal of Image and Graphics 26, no. 6 (2021): 1330–67. http://dx.doi.org/10.11834/jig.210044.

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Chen, Datong, Jean-Marc Odobez, and Hervé Bourlard. "Text detection and recognition in images and video frames." Pattern Recognition 37, no. 3 (2004): 595–608. http://dx.doi.org/10.1016/j.patcog.2003.06.001.

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Ngo, Chong-Wah, and Chi-Kwong Chan. "Video text detection and segmentation for optical character recognition." Multimedia Systems 10, no. 3 (2005): 261–72. http://dx.doi.org/10.1007/s00530-004-0157-0.

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