Text Detection Using Image Processing: A Survey
DOI:
https://doi.org/10.61841/57d4ad27Keywords:
Text Detection, Classification, Preprocessing, Segmentation, Object Detection.Abstract
The aim of text recognition is to recognize the text from written hard copy documents to the required format. The process of text recognition including many steps as well as preprocessing, image segmentation, feature extraction, classification, post-processing. Preprocessing is for doing the essential operation on input image like binarization that converts gray Scale image into Binary Image, noise reduction that removes the noisy signal from an image. Segmentation process for the segment the given image into line by line and segment every character from the segmented line. Future extraction calculates the characteristics of a character. A text classification contains the information and will the comparison. Today, it plays a crucial role within the workplace, university, etc. necessary approaches wont to undergo these stages and their corresponding advantages, disadvantages, and application are presented during this article, numerous text-related applications for imagery also is presented over here. This review performs a comparative analysis of elementary processes during this field
Downloads
References
[1] S. Paudel, P. W. C. Prasad, and A. Alsadoon, Feature Selection Approach for Twitter Sentiment Analysis and
Text Classification Based on Chi-Square and Naïve Bayes. 2018.
[2] J. Kumar, S. Taterh, and D. Kamnthania, “Study and Comparative Analysis of Various Image Spamming
Techniques,” in Advances in Intelligent Systems and Computing, 2018.
[3] S. Yadav and A. K. Thripati, “Comparative analysis of Canny Edge based image steganography with RSA
encryption,” Int. J. Adv. Sci. Technol., 2017.
[4] A. K. Jain and B. B. Gupta, “Phishing detection: Analysis of visual similarity based approaches,” Security and
Communication Networks. 2017.
[5] A. Roy, A. Paul, H. Pirsiavash, and S. Pan, “Automated detection of substance use-related social media posts
based on image and text analysis,” in Proceedings - International Conference on Tools with Artificial
Intelligence, ICTAI, 2018.
[6] S. P. Faustina Joan and S. Valli, “A Survey on Text Information Extraction from Born-Digital and Scene Text
Images,” Proceedings of the National Academy of Sciences India Section A - Physical Sciences. 2019.
[7] R. S. Prasad, S. Prasad, and V. Prasad, “Pattern recognition in thought-form images using radon transform and
histograms,” in ACM International Conference Proceeding Series, 2017.
[8] D. Holland et al., “AUTOMATING THE PHOTOGRAMMETRIC WORKFLOW IN A NATIONAL
MAPPING AGENCY Commission IV , Working Group IV / 3 : Mapping from High Resolution Data,” in
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012.
[9] 9. Retrieved from aarssenl@queensu.ca Aarssen, L. W., & Crimi, L. (2016). Legacy, leisure and the ‘work
hard—Play hard’ hypothesis. The Open Psychology Journal et al., “Playing the objectification game: How
women’s self-esteem impacts the existential consequences of objectification.,” 2018.
[10] M. Shah and R. Kapdi, “Object detection using deep neural networks,” in Proceedings of the 2017
International Conference on Intelligent Computing and Control Systems, ICICCS 2017, 2017.
[11] K. He, X. Zhang, S. Ren, and J. Sun, “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual
Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., 2015.
[12] X. C. Yin, Z. Y. Zuo, S. Tian, and C. L. Liu, “Text Detection, Tracking and Recognition in Video: A
Comprehensive Survey,” IEEE Transactions on Image Processing. 2016.
[13] V. K. Pham and G. S. Lee, “Robust text detection in natural scene images,” in Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
2016.
[14] H. Li, P. Wang, and C. Shen, “Towards End-to-End Text Spotting with Convolutional Recurrent Neural
Networks,” in Proceedings of the IEEE International Conference on Computer Vision, 2017.
[15] L. Neumann and J. Matas, “Scene text localization and recognition with oriented stroke detection,” in
Proceedings of the IEEE International Conference on Computer Vision, 2013.
[16] C. Yao, X. Bai, and W. Liu, “A unified framework for multioriented text detection and recognition,” IEEE
Trans. Image Process., 2014.
[17] C. Bartz, H. Yang, and C. Meinel, “See: Towards semi-supervised end-to-end scene text recognition,” in 32nd
AAAI Conference on Artificial Intelligence, AAAI 2018, 2018.
[18] L. Sun, Q. Huo, W. Jia, and K. Chen, “A robust approach for text detection from natural scene images,”
Pattern Recognit., 2015.
[19] K. Prasad, D. C. Nigam, A. Lakhotiya, D. Umre, and B. I. T. Durg, “Character Recognition Using Matlab ’ s
Neural Network Toolbox,” Int. J. u- e- Serv. Sci. Technol., 2013.
[20] V. Abburu, S. Gupta, S. R. Rimitha, M. Mulimani, and S. G. Koolagudi, “Currency recognition system using
image processing,” in 2017 10th International Conference on Contemporary Computing, IC3 2017, 2018.
[21] J. Galbally, S. Marcel, and J. Fierrez, “Image quality assessment for fake biometric detection: Application to
Iris, fingerprint, and face recognition,” IEEE Trans. Image Process., 2014.
[22] M. Opitz, M. Diem, S. Fiel, F. Kleber, and R. Sablatnig, “End-to-end text recognition using local ternary
patterns, MSER and deep convolutional nets,” in Proceedings - 11th IAPR International Workshop on
Document Analysis Systems, DAS 2014, 2014.
[23] C. Shi, C. Wang, B. Xiao, Y. Zhang, and S. Gao, “Scene text detection using graph model built upon
maximally stable extremal regions,” Pattern Recognit. Lett., 2013.
[24] A. Ramisa, F. Yan, F. Moreno-Noguer, and K. Mikolajczyk, “BreakingNews: Article Annotation by Image and
Text Processing,” IEEE Trans. Pattern Anal. Mach. Intell., 2018.
[25] A. Lavin and S. Gray, “Fast Algorithms for Convolutional Neural Networks,” in Proceedings of the IEEE
Computer Society Conference on Computer Vision and Pattern Recognition, 2016.
[26] S. Emami and V. P. Suciu, “Facial Recognition using OpenCV,” J. Mobile, Embed. Distrib. Syst., 2012.
[27] Y. Lecun, Y. Bengio, and G. Hinton, Artificial Neural Networks as Models of Neural Information Processing.
2015.
[28] G. Li and Y. Yu, “Visual saliency detection based on multiscale deep CNN features,” IEEE Trans. Image
Process., 2016.
[29] T. Kong, A. Yao, Y. Chen, and F. Sun, “HyperNet: Towards accurate region proposal generation and joint
object detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern
Recognition, 2016.
[30] A. Polzounov, A. Ablavatski, S. Escalera, S. Lu, and J. Cai, “Wordfence: Text detection in natural images with
border awareness,” in Proceedings - International Conference on Image Processing, ICIP, 2018.
[31] S. Qin and R. Manduchi, “Cascaded Segmentation-Detection Networks for Word-Level Text Spotting,” in
Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2018.
[32] R. Vedantam, C. L. Zitnick, and D. Parikh, “CIDEr: Consensus-based image description evaluation,” in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015.
[33] M. M. Kasar, D. Bhattacharyya, and T. H. Kim, “Face recognition using neural network: A review,” Int. J.
Secur. its Appl., 2016.
[34] H. Chen, S. S. Tsai, G. Schroth, D. M. Chen, R. Grzeszczuk, and B. Girod, “Robust text detection in natural
images with edge-enhanced maximally stable extremal regions,” in Proceedings - International Conference
on Image Processing, ICIP, 2011.
[35] N. S. Roubtsova, R. G. J. Wijnhoven, and P. H. N. de With, “Integrated text detection and recognition in
natural images,” in Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging
Applications II, 2012.
[36] S. Theodoridis, Machine Learning: A Bayesian and Optimization Perspective. 2015.
[37] R. Raghavendra, K. B. Raja, and C. Busch, “Presentation attack detection for face recognition using light field
camera,” IEEE Trans. Image Process., 2015.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
