Xue, Minglong and Shivakumara, Palaiahnakote and Zhang, Chao and Xiao, Yao and Lu, Tong and Pal, Umapada and Lopresti, Daniel and Yang, Zhibo (2021) Arbitrarily-oriented text detection in low light natural scene images. IEEE Transactions on Multimedia, 23. pp. 2706-2720. ISSN 1520-9210, DOI https://doi.org/10.1109/TMM.2020.3015037.
Full text not available from this repository.Abstract
Text detection in low light natural scene images is challenging due to poor image quality and low contrast. Unlike most existing methods that focus on well-lit (normally daylight) images, the proposed method considers much darker natural scene images. For this task, our method first integrates spatial and frequency domain features through fusion to enhance fine details in the image. Next, we use Maximally Stable Extremal Regions (MSER) for detecting text candidates from the enhanced images. We then introduce Cloud of Line Distribution (COLD) features, which capture the distribution of pixels of text candidates in the polar domain. The extracted features are sent to a Convolution Neural Network (CNN) to correct the bounding boxes for arbitrarily oriented text lines by removing false positives. Experiments are conducted on a dataset of low light images to evaluate the proposed enhancement step. The results show our approach is more effective compared to existing methods in terms of standard quality measures, namely, BRISQE, NIQE and PIQE. In addition, experimental results on a variety of standard benchmark datasets, namely, ICDAR 2013, ICDAR 2015, SVT, Total-Text, ICDAR 2017-MLT and CTW1500, show that the proposed approach not only produces better results for low light images, at the same time it is also competitive for daylight images.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC)[61672273], National Natural Science Foundation of China (NSFC)[61832008], Alibaba Group, Universiti Malaya[GPF014D-2019] |
| Uncontrolled Keywords: | Licenses;Feature extraction;Image segmentation;Proposals; Machine learning;Image enhancement;Convolutional neural networks;Image enhancement;gaussian pyramid filter; Homomorphic filter;COLD features;Convolutional neural network;Arbitrarily-oriented text detection |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 20 Jul 2022 07:53 |
| Last Modified: | 20 Jul 2022 07:53 |
| URI: | http://eprints.um.edu.my/id/eprint/33961 |
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