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Scene Text Segmentation via Multi-Task Cascade Transformer With paired data synthesis

IEEE ACCESS 2023年 11卷 67791-67805页

作者： Dang, Quang-Vinh Lee, Guee-Sang Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju 61186 South Korea

The scene text segmentation task provides a wide range of practical applications. However, the number of images in the available datasets for scene text segmentation is not large enough to effectively train deep learning-based models, leading to limited performance. To solve this problem, we employ paired data generation to secure sufficient data samples for text segmentation via Text Image-conditional GANs. Furthermore, existing models implicitly model text attributes such as size, layout, font, and structure, which hinders their performance. To remedy this, we propose a Multi-task Cascade Transformer network that explicitly learns these attributes using large volumes of generated synthetic data. The transformer-based network includes two auxiliary tasks and one main task for text segmentation. The auxiliary tasks help the network learn text regions to focus on, as well as the structure of the text through different words and fonts, to support the main task. To bridge the gap between different datasets, we train the proposed network on paired synthetic data before fine-tuning it on real data. Our experiments on publicly available scene text segmentation datasets show that our method outperforms existing methods.

关键词： Scene text segmentation paired data synthesis GANs transformer multi-task cascade

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SCENE TEXT SEGMENTATION BY paired data synthesis 30

SCENE TEXT SEGMENTATION BY PAIRED DATA SYNTHESIS

引用

30th IEEE International Conference on Image Processing (ICIP)

作者： Quang-Vinh Dang Lee, Guee-Sang Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea

ISBN: (纸本)9781728198354

Scene text segmentation task has numerous practical applications. However, the number of images in the available datasets for scene text segmentation is too small to effectively train deep learning-based models, leading to limited performance. To solve this problem, we perform the segmentation in two aspects: paired data synthesis and methodology. The former is executed via the proposed Text Image-conditional GANs to generate realistic paired data. We exploit real-world images by self-supervised pre-training scheme via inpainting approach before training the proposed GANs to produce realistic synthetic data. The latter is carried out by the proposed scene text segmentation network to optimize learning the generated paired data, called Multi-task Cascade Transformer. It includes two auxiliary tasks and one main task for text segmentation. The functions of the two auxiliary tasks are to learn the text region to focus on, together with learning the structure of text through their fonts, and then they support the main task. We implement three publicly available datasets for scene text segmentation: ICDAR13 FST, Total Text, and TextSeg datasets to demonstrate the effectiveness of our method. Our experimental result outperforms existing methods.

关键词： Scene Text Segmentation paired data synthesis GANs Transformer Multi-task Cascade

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