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Journal articles on the topic 'Scene Text Recognition'

Author: Grafiati

Published: 20 February 2023

Last updated: 19 July 2025

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1

Chen, Xiaoxue, Lianwen Jin, Yuanzhi Zhu, Canjie Luo, and Tianwei Wang. "Text Recognition in the Wild." ACM Computing Surveys 54, no. 2 (2021): 1–35. http://dx.doi.org/10.1145/3440756.

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The history of text can be traced back over thousands of years. Rich and precise semantic information carried by text is important in a wide range of vision-based application scenarios. Therefore, text recognition in natural scenes has been an active research topic in computer vision and pattern recognition. In recent years, with the rise and development of deep learning, numerous methods have shown promising results in terms of innovation, practicality, and efficiency. This article aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition,

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2

Ratnamala, S. Patil, Hanji Geeta, and Huded Rakesh. "Enhanced scene text recognition using deep learning based hybrid attention recognition network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4927–38. https://doi.org/10.11591/ijai.v13.i4.pp4927-4938.

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The technique of automatically recognizing and transforming text that is present in pictures or scenes into machine-readable text is known as scene text recognition. It facilitates applications like content extraction, translation, and text analysis in real-world visual data by enabling computers to comprehend and extract textual information from images, videos, or documents. Scene text recognition is essential for many applications, such as language translation and content extraction from photographs. The hybrid attention recognition network (HARN), unique technology presented in this researc

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Patil, Ratnamala S., Geeta Hanji, and Rakesh Huded. "Enhanced scene text recognition using deep learning based hybrid attention recognition network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4927. http://dx.doi.org/10.11591/ijai.v13.i4.pp4927-4938.

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<span lang="EN-US">The technique of automatically recognizing and transforming text that is present in pictures or scenes into machine-readable text is known as scene text recognition. It facilitates applications like content extraction, translation, and text analysis in real-world visual data by enabling computers to comprehend and extract textual information from images, videos, or documents. Scene text recognition is essential for many applications, such as language translation and content extraction from photographs. The hybrid attention recognition network (HARN), unique technology

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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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Gao, Yunze, Yingying Chen, Jinqiao Wang, and Hanqing Lu. "Semi-Supervised Scene Text Recognition." IEEE Transactions on Image Processing 30 (2021): 3005–16. http://dx.doi.org/10.1109/tip.2021.3051485.

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6

Singh, Ananta, and Dishant Khosla. "Text Localization and Recognition in Real-Time Scene Images." International Journal of Scientific Engineering and Research 3, no. 5 (2015): 123–25. https://doi.org/10.70729/15051502.

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Yu, Wenhua, Mayire Ibrayim, and Askar Hamdulla. "Scene Text Recognition Based on Improved CRNN." Information 14, no. 7 (2023): 369. http://dx.doi.org/10.3390/info14070369.

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Text recognition is an important research topic in computer vision. Scene text, which refers to the text in real scenes, sometimes needs to meet the requirement of attracting attention, and there is the situation such as deformation. At the same time, the image acquisition process is affected by factors such as occlusion, noise, and obstruction, making scene text recognition tasks more challenging. In this paper, we improve the CRNN model for text recognition, which has relatively low accuracy, poor performance in recognizing irregular text, and only considers obtaining text sequence informati

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Ahmed, Saad, Saeeda Naz, Muhammad Razzak, and Rubiyah Yusof. "Arabic Cursive Text Recognition from Natural Scene Images." Applied Sciences 9, no. 2 (2019): 236. http://dx.doi.org/10.3390/app9020236.

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This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due t

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Liu, Shuhua, Huixin Xu, Qi Li, Fei Zhang, and Kun Hou. "A Robot Object Recognition Method Based on Scene Text Reading in Home Environments." Sensors 21, no. 5 (2021): 1919. http://dx.doi.org/10.3390/s21051919.

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With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning models with high accuracy are adopted to detect and recognize text in multi-view. Second, datasets including 102,000 Chinese and English scene text images and their inverse are generated. The F-measure of text detection is improved by 0.4% and the recognition accuracy is improved by 1.26% because the mod

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Shiravale, Sankirti Sandeep, Jayadevan R, and Sanjeev S. Sannakki. "Recognition of Devanagari Scene Text Using Autoencoder CNN." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 1 (2021): 55–69. http://dx.doi.org/10.5565/rev/elcvia.1344.

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Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(se

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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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Liu, Yiyi, Yuxin Wang, and Hongjian Shi. "A Convolutional Recurrent Neural-Network-Based Machine Learning for Scene Text Recognition Application." Symmetry 15, no. 4 (2023): 849. http://dx.doi.org/10.3390/sym15040849.

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Optical character recognition (OCR) is the process of acquiring text and layout information through analysis and recognition of text data image files. It is also a process to identify the geometric location and orientation of the texts and their symmetrical behavior. It usually consists of two steps: text detection and text recognition. Scene text recognition is a subfield of OCR that focuses on processing text in natural scenes, such as streets, billboards, license plates, etc. Unlike traditional document category photographs, it is a challenging task to use computer technology to locate and

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Liu, Chunmei. "Cascaded Dual-Inpainting Network for Scene Text." Applied Sciences 15, no. 14 (2025): 7742. https://doi.org/10.3390/app15147742.

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Scene text inpainting is a significant research challenge in visual text processing, with critical applications spanning incomplete traffic sign comprehension, degraded container-code recognition, occluded vehicle license plate processing, and other incomplete scene text processing systems. In this paper, a cascaded dual-inpainting network for scene text (CDINST) is proposed. The architecture integrates two scene text inpainting models to reconstruct the text foreground: the Structure Generation Module (SGM) and Structure Reconstruction Module (SRM). The SGM primarily performs preliminary fore

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Wang, Kaili, Yaohua Yi, Junjie Liu, Liqiong Lu, and Ying Song. "Multi-scene ancient chinese text recognition." Neurocomputing 377 (February 2020): 64–72. http://dx.doi.org/10.1016/j.neucom.2019.10.029.

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Li, Shuohao, Anqi Han, Xu Chen, Xiaoqing Yin, and Jun Zhang. "Review network for scene text recognition." Journal of Electronic Imaging 26, no. 05 (2017): 1. http://dx.doi.org/10.1117/1.jei.26.5.053023.

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Wang, Yao, and Jong-Eun Ha. "Scene Text Recognition With Dual Encoders." Journal of Institute of Control, Robotics and Systems 29, no. 12 (2023): 973–79. http://dx.doi.org/10.5302/j.icros.2023.23.0146.

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Albalawi, Bayan M., Amani T. Jamal, Lama A. Al Khuzayem, and Olaa A. Alsaedi. "An End-to-End Scene Text Recognition for Bilingual Text." Big Data and Cognitive Computing 8, no. 9 (2024): 117. http://dx.doi.org/10.3390/bdcc8090117.

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Text localization and recognition from natural scene images has gained a lot of attention recently due to its crucial role in various applications, such as autonomous driving and intelligent navigation. However, two significant gaps exist in this area: (1) prior research has primarily focused on recognizing English text, whereas Arabic text has been underrepresented, and (2) most prior research has adopted separate approaches for scene text localization and recognition, as opposed to one integrated framework. To address these gaps, we propose a novel bilingual end-to-end approach that localize

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18

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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Luan, Xin, Jinwei Zhang, Miaomiao Xu, Wushouer Silamu, and Yanbing Li. "Lightweight Scene Text Recognition Based on Transformer." Sensors 23, no. 9 (2023): 4490. http://dx.doi.org/10.3390/s23094490.

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Scene text recognition (STR) has been a hot research field in computer vision, aiming to recognize text in natural scenes using computers. Currently, attention-based encoder–decoder frameworks struggle to precisely align feature regions with the target object when dealing with complex and low-quality images, a phenomenon known as attention drift. Additionally, with the rise of Transformer, the increasing size of parameters results in higher computational costs. In order to solve the above problems, based on the latest research results of Vision Transformer (ViT), we utilize an additional posit

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20

Huang, Zhiwei, Jinzhao Lin, Hongzhi Yang, et al. "An Algorithm Based on Text Position Correction and Encoder-Decoder Network for Text Recognition in the Scene Image of Visual Sensors." Sensors 20, no. 10 (2020): 2942. http://dx.doi.org/10.3390/s20102942.

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Text recognition in natural scene images has always been a hot topic in the field of document-image related visual sensors. The previous literature mostly solved the problem of horizontal text recognition, but the text in the natural scene is usually inclined and irregular, and there are many unsolved problems. For this reason, we propose a scene text recognition algorithm based on a text position correction (TPC) module and an encoder-decoder network (EDN) module. Firstly, the slanted text is modified into horizontal text through the TPC module, and then the content of horizontal text is accu

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21

Hassan, Ehtesham, and Lekshmi V. L. "Attention Guided Feature Encoding for Scene Text Recognition." Journal of Imaging 8, no. 10 (2022): 276. http://dx.doi.org/10.3390/jimaging8100276.

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The real-life scene images exhibit a range of variations in text appearances, including complex shapes, variations in sizes, and fancy font properties. Consequently, text recognition from scene images remains a challenging problem in computer vision research. We present a scene text recognition methodology by designing a novel feature-enhanced convolutional recurrent neural network architecture. Our work addresses scene text recognition as well as sequence-to-sequence modeling, where a novel deep encoder–decoder network is proposed. The encoder in the proposed network is designed around a hier

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22

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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23

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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Liao, Minghui, Jian Zhang, Zhaoyi Wan, et al. "Scene Text Recognition from Two-Dimensional Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8714–21. http://dx.doi.org/10.1609/aaai.v33i01.33018714.

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Inspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem. Though achieving excellent performance, these methods usually neglect an important fact that text in images are actually distributed in two-dimensional space. It is a nature quite different from that of speech, which is essentially a one-dimensional signal. In principle, directly compressing features of text into a one-dimensional form may lose useful information and introduce extra noise. In this paper, we approach scene text recognition from a two-dimens

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Zhao, Liang, Greg Wilsbacher, and Song Wang. "CommuSpotter: Scene Text Spotting with Multi-Task Communication." Applied Sciences 13, no. 23 (2023): 12540. http://dx.doi.org/10.3390/app132312540.

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Scene text spotting is a challenging multi-task modulation for locating and recognizing texts in complex scenes. Existing end-to-end text spotters generally adopt sequentially decoupled multi-tasks, consisting of text detection and text recognition modules. Although customized modules are designed to connect the tasks closely, there is no interaction among multiple tasks, resulting in compatible information loss for the overall text spotting. Moreover, the independent and sequential modulation is unidirectional, accumulating errors from early to later tasks. In this paper, we propose CommuSpot

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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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Xie, Hongtao, Shancheng Fang, Zheng-Jun Zha, Yating Yang, Yan Li, and Yongdong Zhang. "Convolutional Attention Networks for Scene Text Recognition." ACM Transactions on Multimedia Computing, Communications, and Applications 15, no. 1s (2019): 1–17. http://dx.doi.org/10.1145/3231737.

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Hamad, Moayed, Osama Abu-Elnasr, and Sherif Barakat. "Enhancement Of Text Recognition In Scene Images." Mansoura Journal for Computer and Information Sciences 13, no. 1 (2017): 19–26. http://dx.doi.org/10.21608/mjcis.2017.311954.

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Yang, Xinqi, Wushour Silamu, Miaomiao Xu, and Yanbing Li. "Display-Semantic Transformer for Scene Text Recognition." Sensors 23, no. 19 (2023): 8159. http://dx.doi.org/10.3390/s23198159.

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Linguistic knowledge helps a lot in scene text recognition by providing semantic information to refine the character sequence. The visual model only focuses on the visual texture of characters without actively learning linguistic information, which leads to poor model recognition rates in some noisy (distorted and blurry, etc.) images. In order to address the aforementioned issues, this study builds upon the most recent findings of the Vision Transformer, and our approach (called Display-Semantic Transformer, or DST for short) constructs a masked language model and a semantic visual interactio

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董, 田荣. "Context Attention Network for Scene Text Recognition." Software Engineering and Applications 12, no. 02 (2023): 345–53. http://dx.doi.org/10.12677/sea.2023.122035.

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31

Ding, Liuxu, Yuefeng Liu, Qiyan Zhao, and Yunong Liu. "Text Font Correction and Alignment Method for Scene Text Recognition." Sensors 24, no. 24 (2024): 7917. https://doi.org/10.3390/s24247917.

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Text recognition is a rapidly evolving task with broad practical applications across multiple industries. However, due to the arbitrary-shape text arrangement, irregular text font, and unintended occlusion of font, this remains a challenging task. To handle images with arbitrary-shape text arrangement and irregular text font, we designed the Discriminative Standard Text Font (DSTF) and the Feature Alignment and Complementary Fusion (FACF). To address the unintended occlusion of font, we propose a Dual Attention Serial Module (DASM), which is integrated between residual modules to enhance the f

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Ma, Siliang, and Yong Xu. "Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach." American Journal of Computer Science and Technology 7, no. 3 (2024): 71–81. http://dx.doi.org/10.11648/j.ajcst.20240703.12.

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End-to-end multilingual scene text spotting aims to integrate scene text detection and recognition into a unified framework. Actually, the accuracy of text recognition largely depends on the accuracy of text detection. Due to the lackage of benchmarks with adequate and high-quality character-level annotations for multilingual scene text spotting, most of the existing methods train on the benchmarks only with word-level annotations. However, the performance of multilingual scene text spotting are not that satisfied training on the existing benchmarks, especially for those images with special la

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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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Li, Meiling, Xiumei Li, Junmei Sun, and Yujin Dong. "HRNet Encoder and Dual-Branch Decoder Framework-Based Scene Text Recognition Model." International Journal of Antennas and Propagation 2022 (June 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/2996862.

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Scene text recognition (STR) is designed to automatically recognize the text content in natural scenes. Different from regular document text, text in natural scenes has the characteristics of irregular shapes, complex background, and distorted and blurred contents, which makes STR challenging. To solve the problems of STR for distorted, blurred, and low-resolution texts in natural scenes, this paper proposes a HRNet encoder and dual-branch decoder framework-based STR model. The model mainly consists of an encoder module and a dual-branch decoder module composed of a super-resolution branch and

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Zou, Le, Zhihuang He, Kai Wang, et al. "Text Recognition Model Based on Multi-Scale Fusion CRNN." Sensors 23, no. 16 (2023): 7034. http://dx.doi.org/10.3390/s23167034.

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Scene text recognition is a crucial area of research in computer vision. However, current mainstream scene text recognition models suffer from incomplete feature extraction due to the small downsampling scale used to extract features and obtain more features. This limitation hampers their ability to extract complete features of each character in the image, resulting in lower accuracy in the text recognition process. To address this issue, a novel text recognition model based on multi-scale fusion and the convolutional recurrent neural network (CRNN) has been proposed in this paper. The propose

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Liu, Hao, Bin Wang, Zhimin Bao, et al. "Perceiving Stroke-Semantic Context: Hierarchical Contrastive Learning for Robust Scene Text Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1702–10. http://dx.doi.org/10.1609/aaai.v36i2.20062.

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We introduce Perceiving Stroke-Semantic Context (PerSec), a new approach to self-supervised representation learning tailored for Scene Text Recognition (STR) task. Considering scene text images carry both visual and semantic properties, we equip our PerSec with dual context perceivers which can contrast and learn latent representations from low-level stroke and high-level semantic contextual spaces simultaneously via hierarchical contrastive learning on unlabeled text image data. Experiments in un- and semi-supervised learning settings on STR benchmarks demonstrate our proposed framework can y

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Chen, Jingye, Haiyang Yu, Jianqi Ma, Bin Li, and Xiangyang Xue. "Text Gestalt: Stroke-Aware Scene Text Image Super-resolution." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 285–93. http://dx.doi.org/10.1609/aaai.v36i1.19904.

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In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Li

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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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Jerod, Weinman. "[Rp] Reproducing "Typographical Features for Scene Text Recognition"." ReScience C 6, no. 1 (2020): #. https://doi.org/10.5281/zenodo.4091742.

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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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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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GAO, Song, Chunheng WANG, Baihua XIAO, Cunzhao SHI, Wen ZHOU, and Zhong ZHANG. "Scene Text Character Recognition Using Spatiality Embedded Dictionary." IEICE Transactions on Information and Systems E97.D, no. 7 (2014): 1942–46. http://dx.doi.org/10.1587/transinf.e97.d.1942.

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Wang, Yao, and Jong-Eun Ha. "Scene Text Recognition with Transformer using Multi-patches." Journal of Institute of Control, Robotics and Systems 28, no. 10 (2022): 862–67. http://dx.doi.org/10.5302/j.icros.2022.22.0107.

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Zhang, Xinyun, Binwu Zhu, Xufeng Yao, Qi Sun, Ruiyu Li, and Bei Yu. "Context-Based Contrastive Learning for Scene Text Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3353–61. http://dx.doi.org/10.1609/aaai.v36i3.20245.

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Pursuing accurate and robust recognizers has been a long-lasting goal for scene text recognition (STR) researchers. Recently, attention-based methods have demonstrated their effectiveness and achieved impressive results on public benchmarks. The attention mechanism enables models to recognize scene text with severe visual distortions by leveraging contextual information. However, recent studies revealed that the implicit over-reliance of context leads to catastrophic out-of-vocabulary performance. On the contrary to the superior accuracy of the seen text, models are prone to misrecognize unsee

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Wu, Yan, Jiaxin Fan, Renshuai Tao, et al. "Sequential alignment attention model for scene text recognition." Journal of Visual Communication and Image Representation 80 (October 2021): 103289. http://dx.doi.org/10.1016/j.jvcir.2021.103289.

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Mu, Deguo, Wei Sun, Guoliang Xu, and Wei Li. "Random Blur Data Augmentation for Scene Text Recognition." IEEE Access 9 (2021): 136636–46. http://dx.doi.org/10.1109/access.2021.3117035.

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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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48

Sabir, Ahmed. "Enhancing Scene Text Recognition with Visual Context Information." ACM SIGIR Forum 56, no. 1 (2022): 1–2. http://dx.doi.org/10.1145/3582524.3582542.

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Abstract:

This thesis addresses the problem of improving text spotting systems, which aim to detect and recognize text in unrestricted images (e.g., a street sign, an advertisement, a bus destination, etc.). The goal is to improve the performance of off-the-shelf vision systems by exploiting the semantic information derived from the image itself. The rationale is that knowing the content of the image or the visual context can help to decide which words are the correct candidate words. For example, the fact that an image shows a coffee shop makes it more likely that a word on a signboard reads as Dunkin

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Buoy, Rina, Masakazu Iwamura, Sovila Srun, and Koichi Kise. "Explainable Connectionist-Temporal-Classification-Based Scene Text Recognition." Journal of Imaging 9, no. 11 (2023): 248. http://dx.doi.org/10.3390/jimaging9110248.

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Connectionist temporal classification (CTC) is a favored decoder in scene text recognition (STR) for its simplicity and efficiency. However, most CTC-based methods utilize one-dimensional (1D) vector sequences, usually derived from a recurrent neural network (RNN) encoder. This results in the absence of explainable 2D spatial relationship between the predicted characters and corresponding image regions, essential for model explainability. On the other hand, 2D attention-based methods enhance recognition accuracy and offer character location information via cross-attention mechanisms, linking p

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Xu, Miaomiao, Jiang Zhang, Lianghui Xu, Wushour Silamu, and Yanbing Li. "Collaborative Encoding Method for Scene Text Recognition in Low Linguistic Resources: The Uyghur Language Case Study." Applied Sciences 14, no. 5 (2024): 1707. http://dx.doi.org/10.3390/app14051707.

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Current research on scene text recognition primarily focuses on languages with abundant linguistic resources, such as English and Chinese. In contrast, there is relatively limited research dedicated to low-resource languages. Advanced methods for scene text recognition often employ Transformer-based architectures. However, the performance of Transformer architectures is suboptimal when dealing with low-resource datasets. This paper proposes a Collaborative Encoding Method for Scene Text Recognition in the low-resource Uyghur language. The encoding framework comprises three main modules: the Fi

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