TY  - CONF
AU  - Ayan Banerjee
AU  - Palaiahnakote Shivakumara
AU  - Parikshit Acharya
AU  - Umapada Pal
AU  - Josep Llados
A2  - ICPR
PY  - 2022//
TI  - TWD: A New Deep E2E Model for Text Watermark Detection in Video Images
BT  - 26th International Conference on Pattern Recognition
KW  - Deep learning
KW  - U-Net
KW  - FCENet
KW  - Scene text detection
KW  - Video text detection
KW  - Watermark text detection
N2  - 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 multi-type 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
UR  - https://ieeexplore.ieee.org/document/9956279
UR  - http://dx.doi.org/10.1109/ICPR56361.2022.9956279
N1  - DAG;
ID  - Ayan Banerjee2022
ER  -