Search Results for author: Fengjun Guo

Found 8 papers, 6 papers with code

Visible Watermark Removal via Self-calibrated Localization and Background Refinement

1 code implementation8 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.

Multi-Task Learning

Towards Robust Tampered Text Detection in Document Image: New Dataset and New Solution

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.

Image and Video Forgery Detection Image Compression +1

Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild

1 code implementation23 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).

Optical Character Recognition (OCR)

Inharmonious Region Localization by Magnifying Domain Discrepancy

1 code implementation30 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.

Image Harmonization

DocAligner: Annotating Real-world Photographic Document Images by Simply Taking Pictures

no code implementations9 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.

Self-Supervised Learning

UPOCR: Towards Unified Pixel-Level OCR Interface

no code implementations5 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.

Optical Character Recognition Optical Character Recognition (OCR) +2

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