Mittal, Ayush and Shivakumara, Palaiahnakote and Pal, Umapada and Lu, Tong and Blumenstein, Michael (2022) A new method for detection and prediction of occluded text in natural scene images. Signal Processing: Image Communication, 100. ISSN 0923-5965, DOI https://doi.org/10.1016/j.image.2021.116512.
Full text not available from this repository.Abstract
Text detection from natural scene images is an active research area for computer vision, signal, and image processing because of several real-time applications such as driving vehicles automatically and tracing person behaviors during sports or marathon events. In these situations, there is a high probability of missing text information due to the occlusion of different objects/persons while capturing images. Unlike most of the existing methods, which focus only on text detection by ignoring the effect of missing texts, this work detects and predicts missing texts so that the performance of the OCR improves. The proposed method exploits the property of DCT for finding significant information in images by selecting multiple channels. For chosen DCT channels, the proposed method studies texture distribution based on statistical measurement to extract features. We propose to adopt Bayesian classifier for categorizing text pixels using extracted features. Then a deep learning model is proposed for eliminating false positives to improve text detection performance. Further, the proposed method employs a Natural Language Processing (NLP) model for predicting missing text information by using detected and recognition texts. Experimental results on our dataset, which contains texts occluded by objects, show that the proposed method is effective in predicting missing text information. To demonstrate the effectiveness and objectiveness of the proposed method, we also tested it on the standard datasets of natural scene images, namely, ICDAR 2017-MLT, Total-Text, and CTW1500.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya [GPF014D-2019] |
| Uncontrolled Keywords: | DCT channels; Bayesian classifier; Text detection; Natural language processing; Text restoration; Text prediction |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Jul 2022 06:49 |
| Last Modified: | 22 Jul 2022 06:49 |
| URI: | http://eprints.um.edu.my/id/eprint/33732 |
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