Banerjee, Ayan and Shivakumara, Palaiahnakote and Acharya, Parikshit and Pal, Umapada and Canet, Josep Llados (2022) TWD: a new deep E2E model for text watermark/caption and scene text detection in video. In: 26th International Conference on Pattern Recognition, ICPR 2022, 21-25 August 2022, Montreal.
Full text not available from this repository.Abstract
Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multitype text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | Ministry of Higher Education, Malaysia [Grant no. FRGS/1/2020/ICT02/UM/02/4], Indian Statistical Institute [Grant no. RTI2018-095645-B-C21] |
| Uncontrolled Keywords: | Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 13 Feb 2025 07:18 |
| Last Modified: | 13 Feb 2025 07:18 |
| URI: | http://eprints.um.edu.my/id/eprint/40463 |
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