Log in

Relevant bibliographies by topics / Text detection and recognition / Journal articles

To see the other types of publications on this topic, follow the link: Text detection and recognition.

Journal articles on the topic 'Text detection and recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Text detection and recognition.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

11

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

., Shivani, and Dipti Bansal. "Techniques of Text Detection and Recognition: A Survey." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 83. http://dx.doi.org/10.23956/ijermt.v6i6.250.

Full text

Abstract:

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 reviewed and discussed in terms of their outcomes.

APA, Harvard, Vancouver, ISO, and other styles

13

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 (January 31, 2019): 546–51. http://dx.doi.org/10.26438/ijcse/v7i1.546551.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

BAI, Xiang, Minghui LIAO, Baoguang SHI, and Mingkun YANG. "Deep learning for scene text detection and recognition." SCIENTIA SINICA Informationis 48, no. 5 (May 1, 2018): 531–44. http://dx.doi.org/10.1360/n112018-00003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Ye, Qixiang, and David Doermann. "Text Detection and Recognition in Imagery: A Survey." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 7 (July 1, 2015): 1480–500. http://dx.doi.org/10.1109/tpami.2014.2366765.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Murugan, S., and R. Karthika. "A Survey on Traffic Sign Detection Techniques Using Text Mining." Asian Journal of Computer Science and Technology 8, S1 (February 5, 2019): 21–24. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1975.

Full text

Abstract:

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 perform some functions on it, in order to get an enhanced image or to extract some useful information from it.

APA, Harvard, Vancouver, ISO, and other styles

17

Qiao, Liang, Sanli Tang, Zhanzhan Cheng, Yunlu Xu, Yi Niu, Shiliang Pu, and Fei Wu. "Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11899–907. http://dx.doi.org/10.1609/aaai.v34i07.6864.

Full text

Abstract:

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 employs an efficient segmentation-based text detector that learns the latent text reading order and boundary information. Then a novel Shape Transform Module (abbr. STM) is designed to transform the detected feature regions into regular morphologies without extra parameters. It unites text detection and the following recognition part into a whole framework, and helps the whole network achieve global optimization. Experiments show that our method achieves competitive performance on two standard text benchmarks, i.e., ICDAR 2013 and ICDAR 2015, and also obviously outperforms existing methods on irregular text benchmarks SCUT-CTW1500 and Total-Text.

APA, Harvard, Vancouver, ISO, and other styles

18

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 (June 30, 2018): 341–45. http://dx.doi.org/10.26438/ijcse/v6i6.341345.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

R., Sayali, and Sankirti S. "Restoration of Degraded Images for Text Detection and Recognition." International Journal of Computer Applications 134, no. 4 (January 15, 2016): 25–29. http://dx.doi.org/10.5120/ijca2016907895.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Yang, Haojin, Bernhard Quehl, and Harald Sack. "A framework for improved video text detection and recognition." Multimedia Tools and Applications 69, no. 1 (October 11, 2012): 217–45. http://dx.doi.org/10.1007/s11042-012-1250-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

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.

Full text

Abstract:

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 could achieve better detection rate than others.

APA, Harvard, Vancouver, ISO, and other styles

26

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 (October 2021): 39–62. http://dx.doi.org/10.4018/ijcvip.2021100104.

Full text

Abstract:

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, cognitive reading for image processing, maximally stable extreme regions, stroke width transformation, and achieved remarkable results up to 90.34% of F-score with benchmark datasets such as ICDAR 2013, ICDAR 2019, IIIT5k. The researchers have done outstanding work in the text recognition field. Yet, improvement in text detection in low-quality image performance is required, as text identification should not be limited to the input quality of the image.

APA, Harvard, Vancouver, ISO, and other styles

27

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 (June 30, 2021): 2894–99. http://dx.doi.org/10.22214/ijraset.2021.35616.

Full text

Abstract:

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 traditional techniques are Character extraction, Character recognition, and Feature extraction, while the modern techniques are segmenting the lines for recognition, machine learning techniques, convolution neural networks, and recurrent neural networks. There are various applications for the HTR/HWR such as the Online recognition, Offline Recognition, Signature verification, Postal address interpretation, Bank-Cheque processing, Writer recognition and these are considered to be the active areas of research. An effective HTR/HWR is therefore needed for the above stated applications. During this project our objective is to find and develop various models of the purpose.

APA, Harvard, Vancouver, ISO, and other styles

28

Li, Haiyan, and Hongtao Lu. "AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection." Future Internet 12, no. 11 (November 17, 2020): 200. http://dx.doi.org/10.3390/fi12110200.

Full text

Abstract:

Text detection is a prerequisite for text recognition in scene images. Previous segmentation-based methods for detecting scene text have already achieved a promising performance. However, these kinds of approaches may produce spurious text instances, as they usually confuse the boundary of dense text instances, and then infer word/text line instances relying heavily on meticulous heuristic rules. We propose a novel Assembling Text Components (AT-text) that accurately detects dense text in scene images. The AT-text localizes word/text line instances in a bottom-up mechanism by assembling a parsimonious component set. We employ a segmentation model that encodes multi-scale text features, considerably improving the classification accuracy of text/non-text pixels. The text candidate components are finely classified and selected via discriminate segmentation results. This allows the AT-text to efficiently filter out false-positive candidate components, and then to assemble the remaining text components into different text instances. The AT-text works well on multi-oriented and multi-language text without complex post-processing and character-level annotation. Compared with the existing works, it achieves satisfactory results and a considerable balance between precision and recall without a large margin in ICDAR2013 and MSRA-TD 500 public benchmark datasets.

APA, Harvard, Vancouver, ISO, and other styles

29

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 (April 2014): 34–63. http://dx.doi.org/10.4018/ijhisi.2014040103.

Full text

Abstract:

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 biomedical publication images was investigated. This paper presents both image analysis and feature-based approaches to extract and recognize specific regions of interest (text labels) within images in biomedical publications. Experiments were performed on 6515 characters extracted from text labels present in 200 biomedical publication images. These images are part of the data set from ImageCLEF 2010. Automated character recognition experiments were conducted using geometry-, region-, exemplar-, and profile-based correlation features and Fourier descriptors extracted from the characters. Correct recognition as high as 92.67% was obtained with a support vector machine classifier, compared to a 75.90% correct recognition rate with a benchmark Optical Character Recognition technique.

APA, Harvard, Vancouver, ISO, and other styles

30

Ren, Xiaohang, Yi Zhou, Zheng Huang, Jun Sun, Xiaokang Yang, and Kai Chen. "A Novel Text Structure Feature Extractor for Chinese Scene Text Detection and Recognition." IEEE Access 5 (2017): 3193–204. http://dx.doi.org/10.1109/access.2017.2676158.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

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 (October 31, 2019): 1248. http://dx.doi.org/10.3390/electronics8111248.

Full text

Abstract:

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 supplement to the speech recognition technology. In this paper, a text processing model after Chinese Speech Recognition is proposed, which combines a bidirectional long short-term memory (LSTM) network with a conditional random field (CRF) model. The task is divided into two stages: text error detection and text error correction. In this paper, a bidirectional long short-term memory (Bi-LSTM) network and conditional random field are used in two stages of text error detection and text error correction respectively. Through verification and system test on the SIGHAN 2013 Chinese Spelling Check (CSC) dataset, the experimental results show that the model can effectively improve the accuracy of text after speech recognition.

APA, Harvard, Vancouver, ISO, and other styles

32

Wu, Weijia, Jici Xing, Cheng Yang, Yuxing Wang, and Hong Zhou. "A Scene Text Detector for Text with Arbitrary Shapes." Mathematical Problems in Engineering 2020 (June 11, 2020): 1–11. http://dx.doi.org/10.1155/2020/8916028.

Full text

Abstract:

The performance of text detection is crucial for the subsequent recognition task. Currently, the accuracy of the text detector still needs further improvement, particularly those with irregular shapes in a complex environment. We propose a pixel-wise method based on instance segmentation for scene text detection. Specifically, a text instance is split into five components: a Text Skeleton and four Directional Pixel Regions, then restoring itself based on these elements and receiving supplementary information from other areas when one fails. Besides, a Confidence Scoring Mechanism is designed to filter characters similar to text instances. Experiments on several challenging benchmarks demonstrate that our method achieves state-of-the-art results in scene text detection with an F-measure of 84.6% on Total-Text and 86.3% on CTW1500.

APA, Harvard, Vancouver, ISO, and other styles

33

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 (November 27, 2022): 12120. http://dx.doi.org/10.3390/app122312120.

Full text

Abstract:

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 Stable Extreme Regions) method to fully consider color information in text detection, which separates the text area in the image from the complex background, effectively reducing the influence of the complex background and light on street sign text detection. We also propose an enhanced feature pyramid network text detection method, which includes a feature pyramid route enhancement (FPRE) module and a high-level feature enhancement (HLFE) module. The two modules can make full use of the network’s low-level and high-level semantic information to enhance the network’s effectiveness in localizing text information and detecting text with different shapes, sizes, and inclined text. Experiments showed that the F-scores obtained by the method proposed in this paper on ICDAR 2015 (International Conference on Document Analysis and Recognition 2015) dataset, ICDAR2017-MLT (International Conference on Document Analysis and Recognition 2017- Competition on Multi-lingual scene text detection) dataset, and the Natural Scene Street Signs (NSSS) dataset constructed in this study are 89.5%, 84.5%, and 73.3%, respectively, which confirmed the performance advantage of the method proposed in street sign text detection.

APA, Harvard, Vancouver, ISO, and other styles

34

Francisca O Nwokoma, Juliet N Odii, Ikechukwu I Ayogu, and James C Ogbonna. "Camera-based OCR scene text detection issues: A review." World Journal of Advanced Research and Reviews 12, no. 3 (December 30, 2021): 484–89. http://dx.doi.org/10.30574/wjarr.2021.12.3.0705.

Full text

Abstract:

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 traditional scanner whose usability is limited as they are not portable in size and cannot be used on images captured by cameras. However, characters captured by traditional scanners pose fewer computational difficulties as compared to camera captured images that are associated with divers’ challenges with consequences of high computational complexity and recognition difficulties. This paper, therefore, reviews the various factors that increase the computational difficulties of Camera-Based OCR, and made some recommendations as per the best practices for Camera-Based OCR systems.

APA, Harvard, Vancouver, ISO, and other styles

35

Kamble, Prathamesh, Rohit Pisal, Hrutik Khade, Vishal Sole, and Prof S. R. Bhujbal. "Automated Vehicle Number Plate Detection and Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 1307–11. http://dx.doi.org/10.22214/ijraset.2023.48785.

Full text

Abstract:

Abstract: In this project, a Digital Image Processing-based prototype is developed. Actions such as Image Acquisition, enhancement that is pre-processing, Segmentation of the license plate and then application of OCR (Optical Character Recognition) is applied to store the number on text form. The plate number is displayed as text on the terminal using the principle of OCR with help of Tesseract engine. It is seen that the security forces and authorities face problems whenever security forces chase a vehicle or they can’t catch a vehicle which broke traffic rules. Authorities find it very hectic on a busy day to log the vehicle numbers manually in a parking lot.

APA, Harvard, Vancouver, ISO, and other styles

36

Zhang, Ziheng. "Review of text emotion detection." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 213–21. http://dx.doi.org/10.54097/hset.v12i.1456.

Full text

Abstract:

Emotion is one of the essential characteristics of being human. When writing essays or reports, people will add their own emotions. Text sentiment detection can detect the leading emotional tone of a text. Text emotion detection and recognition is a new research field related to sentiment analysis. Emotion analysis detects and identifies emotion types, such as anger, happiness, or sadness, through textual expression. It is a subdomain of NLP. For some applications, the technology could help large companies' Chinese and Russian data analysts gauge public opinion or conduct nuanced market research and understand product reputation. At present, text emotion is one of the most studied fields in the literature. Still, it is also tricky because it is related to deep neural networks and requires the application of psychological knowledge. In this article, we will discuss the concept of text detection and introduce and analyze the main methods of text emotion detection. In addition, this paper will also discuss the advantages and weaknesses of this technology and some future research directions and problems to be solved.

APA, Harvard, Vancouver, ISO, and other styles

37

Sun, Weiwei, Huiqian Wang, Yi Lu, Jiasai Luo, Ting Liu, Jinzhao Lin, Yu Pang, and Guo Zhang. "Deep-Learning-Based Complex Scene Text Detection Algorithm for Architectural Images." Mathematics 10, no. 20 (October 21, 2022): 3914. http://dx.doi.org/10.3390/math10203914.

Full text

Abstract:

With the advent of smart cities, the text information in an image can be accurately located and recognized, and then applied to the fields of instant translation, image retrieval, card surface information recognition, and license plate recognition. Thus, people’s lives and work will become more convenient and comfortable. Owing to the varied orientations, angles, and shapes of text, identifying textual features from images is challenging. Therefore, we propose an improved EAST detector algorithm for detecting and recognizing slanted text in images. The proposed algorithm uses reinforcement learning to train a recurrent neural network controller. The optimal fully convolutional neural network structure is selected, and multi-scale features of text are extracted. After importing this information into the output module, the Generalized Intersection over Union algorithm is used to enhance the regression effect of the text bounding box. Next, the loss function is adjusted to ensure a balance between positive and negative sample classes before outputting the improved text detection results. Experimental results indicate that the proposed algorithm can address the problem of category homogenization and improve the low recall rate in target detection. When compared with other image detection algorithms, the proposed algorithm can better identify slanted text in natural scene images. Finally, its ability to recognize text in complex environments is also excellent.

APA, Harvard, Vancouver, ISO, and other styles

38

Sudir, P., and M. Ravishankar. "An Effective Edge and Texture Based Approach towards Curved Videotext Detection and Extraction." International Journal of System Dynamics Applications 4, no. 3 (July 2015): 1–29. http://dx.doi.org/10.4018/ijsda.2015070101.

Full text

Abstract:

In present day video text greatly helps video indexing and retrieval system as they often carry significant semantic information. Video text analysis is challenging due to varying background, multiple orientations and low contrast between text and non-text regions. Proposed approach explores a new framework for curved video text detection and recognition where from the observation that curve text regions can be well defined by edges size and uniform texture, Probable curved text edge detection is accomplished by processing wavelet sub bands followed by text localization by utilizing fast texture descriptor LU-transform. Binarization is achieved by maximal H-transform. A Connected Component filtering method followed by B-Spline curve fitting on centroid of each character vertically aligns each oriented character. The aligned text string is recognized by optical character recognition (OCR). Experiments on various curved video frames shows that proposed method is efficacious and robust in detecting and recognizing curved videotext.

APA, Harvard, Vancouver, ISO, and other styles

39

R. Sanjuna, K., and K. Dinakaran. "A Multi-Object Feature Selection Based Text Detection and Extraction Using Skeletonized Region Optical Character Recognition in-Text Images." International Journal of Engineering & Technology 7, no. 3.6 (July 4, 2018): 386. http://dx.doi.org/10.14419/ijet.v7i3.6.16009.

Full text

Abstract:

Information or content extraction from image is crucial task for obtaining text in natural scene images. The problem arise due to variation in images contains differential object to explore values like, background filling, saturation ,color etc. text projections from different styles varies the essential information which is for wrong understand for detecting characters.so detection of region text need more accuracy to identify the exact object. To consider this problem, to propose a multi-objective feature for text detection and localization based on skeletonized text bound box region of text confidence score. This contributes the intra edge detection, segmentation along skeleton of object reflective. the impact of multi-objective region selection model (MSOR) is to recognize the exact character of style matches using the bounding box region analysis which is to identify the object portion to accomplish the candidate extraction model.To enclose the text region localization of text resolution and hazy image be well identified edge smoothing quick guided filter methods. Further the region are skeletonized to morphing the segmented region of inter segmentation to extract the text.

APA, Harvard, Vancouver, ISO, and other styles

40

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.

Full text

Abstract:

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 part is the elaboration of some main technical research routes of scene text detection. According to the timeline of the detection methods, the specific contents of various text detection models are further introduced. Thirdly, this paper compares and analyzes the experimental results of different models. Furthermore, improvements of some models with relationship, effects, advantages and disadvantages and expectations are further introduced. Finally, the challenges and development trends of scene text detection technology based on deep learning are summarized.

APA, Harvard, Vancouver, ISO, and other styles

41

Ma, Xiaoli, Hongyan Xu, Xiaoqian Zhang, and Haoyong Wang. "An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition." Complexity 2021 (February 12, 2021): 1–11. http://dx.doi.org/10.1155/2021/6617799.

Full text

Abstract:

With the rapid development of artificial intelligence technology, multitasking textual translation has attracted more and more attention. Especially after the application of deep learning technology, the performance of multitask translation text detection and recognition has been greatly improved. However, because multitasking contains the interference problem faced by the translated text, there is a big gap between recognition performance and actual application requirements. Aiming at multitasking and translation text detection, this paper proposes a text localization method based on multichannel multiscale detection of the largest stable extreme value region and cascade filtering. This paper selects the appropriate color channel and scale to extract the maximum stable extreme value area as the character candidate area and designs a cascaded filter from coarse to fine to remove false detections. The coarse filter is based on some simple morphological features and stroke width features, and the fine filter is trained by a two-recognition convolutional neural network. The remaining character candidate regions are merged into horizontal or multidirectional character strings through the graph model. The experimental results on the text data set prove the effectiveness of the improved deep learning network character model and the feasibility of the textual implication translation analysis method based on this model. Among them, the text contains translation character recognition results prove that the model has good description ability. The characteristics of the model determine that this method is not sensitive to the scale of the sliding window, so it performs better than the existing typical methods in retrieval tasks.

APA, Harvard, Vancouver, ISO, and other styles

42

Du, Yangtao, Xu Zhang, Guoce Zhang, and Wei Sun. "Detection and recognition of text traffic signs above the road." International Journal of Sensor Networks 35, no. 2 (2021): 69. http://dx.doi.org/10.1504/ijsnet.2021.10036227.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Sun, Wei, Yangtao Du, Xu Zhang, and Guoce Zhang. "Detection and recognition of text traffic signs above the road." International Journal of Sensor Networks 35, no. 2 (2021): 69. http://dx.doi.org/10.1504/ijsnet.2021.113626.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Kherde, Rutuja. "Text Detection and Recognition Techniques from Images using React Native." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 2 (April 25, 2020): 1562–67. http://dx.doi.org/10.30534/ijatcse/2020/100922020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Godha, Ashima, and Rahul Sharma. "Comparative Study on Text Detection and Recognition from Lecture Videos." International Journal of Information Systems and Computer Sciences 8, no. 2 (April 15, 2019): 1–6. http://dx.doi.org/10.30534/ijiscs/2019/01822019.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Yin, Xu-Cheng, Ze-Yu Zuo, Shu Tian, and Cheng-Lin Liu. "Text Detection, Tracking and Recognition in Video: A Comprehensive Survey." IEEE Transactions on Image Processing 25, no. 6 (June 2016): 2752–73. http://dx.doi.org/10.1109/tip.2016.2554321.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Yu, Chong, Yonghong Song, Quan Meng, Yuanlin Zhang, and Yang Liu. "Text detection and recognition in natural scene with edge analysis." IET Computer Vision 9, no. 4 (August 2015): 603–13. http://dx.doi.org/10.1049/iet-cvi.2013.0307.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Fraz, Muhammad, M. Saquib Sarfraz, and Eran A. Edirisinghe. "Exploiting colour information for better scene text detection and recognition." International Journal on Document Analysis and Recognition (IJDAR) 18, no. 2 (February 19, 2015): 153–67. http://dx.doi.org/10.1007/s10032-015-0239-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Zhu, Yingying, Cong Yao, and Xiang Bai. "Scene text detection and recognition: recent advances and future trends." Frontiers of Computer Science 10, no. 1 (June 22, 2015): 19–36. http://dx.doi.org/10.1007/s11704-015-4488-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

López-Úbeda, Pilar, Manuel Carlos Díaz-Galiano, Teodoro Martín-Noguerol, Antonio Luna, L. Alfonso Ureña-López, and M. Teresa Martín-Valdivia. "COVID-19 detection in radiological text reports integrating entity recognition." Computers in Biology and Medicine 127 (December 2020): 104066. http://dx.doi.org/10.1016/j.compbiomed.2020.104066.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!