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Text Detection and Classification from Low Quality Natural Images

Ujala Yasmeen1, Jamal Hussain Shah1, Muhammad Attique Khan2, Ghulam Jillani Ansari1, Saeed ur Rehman1, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*

1 Department of Computer Science, Wah Campus, COMSATS University Islamabad, Islamabad, 47040, Pakistan
2 Department of Computer Science, HITEC University, Taxila, 47080, Pakistan
3 Department of Mathematics and Computer Science, Beirut Arab University, Beirut, Lebanon
4 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, South Korea

* Corresponding Author: Yunyoung Nam. Email: email

(This article belongs to this Special Issue: Recent Trends in Artificial Intelligence for Automated Complex Industrial Systems)

Intelligent Automation & Soft Computing 2020, 26(6), 1251-1266.


Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image. (b) Subsequently, the standard maximal stable extreme regions (MSER) technique is applied to localize and detect text. Soon after, K-Means is applied to get three different clusters of the query image to separate foreground and background and also incorporate character level grouping. (c) Finally, the segmented text is classified into textual and non-textual regions using a novel convolutional neural network (CNN) framework. The purpose of this task is to overcome the false positives. For evaluation of proposed technique, results are obtained on three mainstream datasets, including SVT, IIIT5K and ICDAR 2003. The achieved classification results of 90.3% for SVT dataset, 95.8% for IIIT5K dataset, and 94.0% for the ICDAR 2003 dataset, respectively. It shows the preeminence of the proposed methodology that it works fine for good model learning. Finally, the proposed methodology is compared with previous benchmark text-detection techniques to validate its contribution.


Cite This Article

U. Yasmeen, J. Hussain Shah, M. Attique Khan, G. Jillani Ansari, S. Ur Rehman et al., "Text detection and classification from low quality natural images," Intelligent Automation & Soft Computing, vol. 26, no.6, pp. 1251–1266, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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