no code implementations • 5 Dec 2023 • Dezhi Peng, Zhenhua Yang, Jiaxin Zhang, Chongyu Liu, Yongxin Shi, Kai Ding, Fengjun Guo, Lianwen Jin
Without bells and whistles, the experimental results showcase that the proposed method can simultaneously achieve state-of-the-art performance on three tasks with a unified single model, which provides valuable strategies and insights for future research on generalist OCR models.
1 code implementation • ICCV 2023 • Li Niu, Linfeng Tan, Xinhao Tao, Junyan Cao, Fengjun Guo, Teng Long, Liqing Zhang
Given a composite image, image harmonization aims to adjust the foreground illumination to be consistent with background.
no code implementations • 9 Jun 2023 • Jiaxin Zhang, Bangdong Chen, Hiuyi Cheng, Fengjun Guo, Kai Ding, Lianwen Jin
Furthermore, considering the importance of fine-grained elements in document images, we present a details recurrent refinement module to enhance the output in a high-resolution space.
1 code implementation • CVPR 2023 • Chenfan Qu, Chongyu Liu, Yuliang Liu, Xinhong Chen, Dezhi Peng, Fengjun Guo, Lianwen Jin
In this paper, we propose a novel framework to capture more fine-grained clues in complex scenarios for tampered text detection, termed as Document Tampering Detector (DTD), which consists of a Frequency Perception Head (FPH) to compensate the deficiencies caused by the inconspicuous visual features, and a Multi-view Iterative Decoder (MID) for fully utilizing the information of features in different scales.
1 code implementation • 30 Sep 2022 • Jing Liang, Li Niu, Penghao Wu, Fengjun Guo, Teng Long
Inharmonious region localization aims to localize the region in a synthetic image which is incompatible with surrounding background.
1 code implementation • 23 Jul 2022 • Jiaxin Zhang, Canjie Luo, Lianwen Jin, Fengjun Guo, Kai Ding
To address this issue, we propose a novel approach called Marior (Margin Removal and \Iterative Content Rectification).
1 code implementation • 21 Jul 2022 • Chongyu Liu, Lianwen Jin, Yuliang Liu, Canjie Luo, Bangdong Chen, Fengjun Guo, Kai Ding
To address this issue, we propose a Contextual-guided Text Removal Network, termed as CTRNet.
1 code implementation • 8 Aug 2021 • Jing Liang, Li Niu, Fengjun Guo, Teng Long, Liqing Zhang
In the refinement stage, we integrate multi-level features to improve the texture quality of watermarked area.