4 code implementations • 29 Jul 2022 • Dezhi Peng, Lianwen Jin, Yuliang Liu, Canjie Luo, Songxuan Lai
Utilizing the proposed weakly supervised learning framework, PageNet requires only transcripts to be annotated for real data; however, it can still output detection and recognition results at both the character and line levels, avoiding the labor and cost of labeling bounding boxes of characters and text lines.
1 code implementation • 28 Jul 2022 • Song Tao, Zijian Wang, Tiantian Fan, Canjie Luo, Can Huang
In this paper, we focus on the embedding learning of word blocks containing text and layout information, and propose UTel, a language model with Unified TExt and Layout pre-training.
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 • CVPR 2022 • Yuxin Kong, Canjie Luo, Weihong Ma, Qiyuan Zhu, Shenggao Zhu, Nicholas Yuan, Lianwen Jin
Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures.
1 code implementation • CVPR 2022 • Canjie Luo, Lianwen Jin, Jingdong Chen
Motivated by this common sense, we augment one image patch and use its neighboring patch as guidance to recover itself.
no code implementations • 23 Feb 2022 • Canjie Luo, Yuanzhi Zhu, Lianwen Jin, Zhe Li, Dezhi Peng
Specifically, we propose a style bank to parameterize the specific handwriting styles as latent vectors, which are input to a generator as style priors to achieve the corresponding handwritten styles.
1 code implementation • CVPR 2021 • Tianwei Wang, Yuanzhi Zhu, Lianwen Jin, Dezhi Peng, Zhe Li, Mengchao He, Yongpan Wang, Canjie Luo
Specifically, we integrate IFA into the two most prevailing text recognition streams (attention-based and CTC-based) and propose attention-guided dense prediction (ADP) and Extended CTC (ExCTC).
Optical Character Recognition
Optical Character Recognition (OCR)
+1
1 code implementation • 7 May 2020 • Xiaoxue Chen, Lianwen Jin, Yuanzhi Zhu, Canjie Luo, Tianwei Wang
This paper aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition; (2) introduce new insights and ideas; (3) provide a comprehensive review of publicly available resources; (4) point out directions for future work.
3 code implementations • CVPR 2020 • Canjie Luo, Yuanzhi Zhu, Lianwen Jin, Yongpan Wang
An agent network learns from the output of the recognition network and controls the fiducial points to generate more proper training samples for the recognition network.
no code implementations • CVPR 2020 • Xinyu Wang, Yuliang Liu, Chunhua Shen, Chun Chet Ng, Canjie Luo, Lianwen Jin, Chee Seng Chan, Anton Van Den Hengel, Liangwei Wang
Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize.
no code implementations • 13 Jan 2020 • Canjie Luo, Qingxiang Lin, Yuliang Liu, Lianwen Jin, Chunhua Shen
Furthermore, to tackle the issue of lacking paired training samples, we design an interactive joint training scheme, which shares attention masks from the recognizer to the discriminator, and enables the discriminator to extract the features of each character for further adversarial training.
5 code implementations • 21 Dec 2019 • Tianwei Wang, Yuanzhi Zhu, Lianwen Jin, Canjie Luo, Xiaoxue Chen, Yaqiang Wu, Qianying Wang, Mingxiang Cai
To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results.
Ranked #4 on
Scene Text Recognition
on ICDAR 2003
1 code implementation • 20 Dec 2019 • Yuliang Liu, Tong He, Hao Chen, Xinyu Wang, Canjie Luo, Shuaitao Zhang, Chunhua Shen, Lianwen Jin
More importantly, based on OBD, we provide a detailed analysis of the impact of a collection of refinements, which may inspire others to build state-of-the-art text detectors.
Ranked #3 on
Scene Text Detection
on ICDAR 2017 MLT
1 code implementation • 17 Sep 2019 • Yipeng Sun, Zihan Ni, Chee-Kheng Chng, Yuliang Liu, Canjie Luo, Chun Chet Ng, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin
Robust text reading from street view images provides valuable information for various applications.
1 code implementation • 16 Sep 2019 • Chee-Kheng Chng, Yuliang Liu, Yipeng Sun, Chun Chet Ng, Canjie Luo, Zihan Ni, ChuanMing Fang, Shuaitao Zhang, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin
This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting.
no code implementations • 26 Aug 2019 • Xiaoxue Chen, Tianwei Wang, Yuanzhi Zhu, Lianwen Jin, Canjie Luo
Scene text recognition has attracted particular research interest because it is a very challenging problem and has various applications.
1 code implementation • CVPR 2019 • Yuliang Liu, Lianwen Jin, Zecheng Xie, Canjie Luo, Shuaitao Zhang, Lele Xie
Evaluation protocols play key role in the developmental progress of text detection methods.
7 code implementations • 10 Jan 2019 • Canjie Luo, Lianwen Jin, Zenghui Sun
It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text.
no code implementations • 12 Nov 2017 • Sheng Zhang, Yuliang Liu, Lianwen Jin, Canjie Luo
In this paper, we propose a refined scene text detector with a \textit{novel} Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement.