Mokayed, Hamam and Shivakumara, Palaiahnakote and Saini, Rajkumar and Liwicki, Marcus and Hin, Loo Chee and Pal, Umapada (2021) Anomaly detection in natural scene images based on enhanced fine-grained saliency and fuzzy logic. IEEE Access, 9. pp. 129102-129109. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2021.3103279.
Full text not available from this repository.Abstract
This paper proposes a simple yet effective method for anomaly detection in natural scene images improving natural scene text detection and recognition. In the last decade, there has been significant progress towards text detection and recognition in natural scene images. However, in cases where there are logos, company symbols, or other decorative elements for text, existing methods do not perform well. This work considers such misclassified components, which are part of the text as anomalies, and presents a new idea for detecting such anomalies in the text for improving text detection and recognition in natural scene images. The proposed method considers the result of the existing text detection method as input for segmenting characters or components based on saliency map and rough set theory. For each segmented component, the proposed method extracts feature from the saliency map based on density, pixel distribution, and phase congruency to classify text and non-text components by exploring a fuzzy-based classifier. To verify the effectiveness of the method, we have performed experiments on several benchmark datasets of natural scene text detection, namely, MSRATD-500 and SVT. Experimental results show the efficacy of the proposed method over the existing ones for text detection and recognition in these datasets.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Text recognition; Feature extraction; Image segmentation; Deep learning; Rough sets; Shape; Character recognition; Natural scene detection; Natural scene text recognition; Rough set; Saliency; Fuzzy logic; Anomaly text classification |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 03 Mar 2022 03:30 |
| Last Modified: | 03 Mar 2022 03:30 |
| URI: | http://eprints.um.edu.my/id/eprint/28256 |
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