48 citations · 166 across the 19 of their papers we have counts for
20 papers
PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition
Dezhi Peng, Lianwen Jin, Yuliang Liu +2
Handwritten Chinese text recognition (HCTR) has been an active research topic for decades. However, most previous studies solely focus on the recognition of cropped text line image…
Knowing Where and What: Unified Word Block Pretraining for Document Understanding
Song Tao, Zijian Wang, Tiantian Fan +2
Due to the complex layouts of documents, it is challenging to extract information for documents. Most previous studies develop multimodal pre-trained models in a self-supervised wa…
Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild
Jiaxin Zhang, Canjie Luo, Lianwen Jin +2
Camera-captured document images usually suffer from perspective and geometric deformations. It is of great value to rectify them when considering poor visual aesthetics and the det…
Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context
Chongyu Liu, Lianwen Jin, Yuliang Liu +4
Text removal has attracted increasingly attention due to its various applications on privacy protection, document restoration, and text editing. It has shown significant progress w…
Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator
Yuxin Kong, Canjie Luo, Weihong Ma +4
Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures. Typically, only a few samples can serve as the st…
SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization
Canjie Luo, Lianwen Jin, Jingdong Chen
Recently self-supervised representation learning has drawn considerable attention from the scene text recognition community. Different from previous studies using contrastive learn…
SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text
Canjie Luo, Yuanzhi Zhu, Lianwen Jin +2
Large amounts of labeled data are urgently required for the training of robust text recognizers. However, collecting handwriting data of diverse styles, along with an immense lexic…
Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter
Tianwei Wang, Yuanzhi Zhu, Lianwen Jin +5
Text recognition is a popular research subject with many associated challenges. Despite the considerable progress made in recent years, the text recognition task itself is still co…
Text Recognition in the Wild: A Survey
Xiaoxue Chen, Lianwen Jin, Yuanzhi Zhu +2
The history of text can be traced back over thousands of years. Rich and precise semantic information carried by text is important in a wide range of vision-based application scena…
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
Canjie Luo, Yuanzhi Zhu, Lianwen Jin +1
Handwritten text and scene text suffer from various shapes and distorted patterns. Thus training a robust recognition model requires a large amount of data to cover diversity as mu…
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering
Xinyu Wang, Yuliang Liu, Chunhua Shen +6
Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize. This is visible in the fact that they are vulnerable to learning coi…
Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild
Canjie Luo, Qingxiang Lin, Yuliang Liu +2
We propose to improve text recognition from a new perspective by separating the text content from complex backgrounds. As vanilla GANs are not sufficiently robust to generate seque…
Decoupled Attention Network for Text Recognition
Tianwei Wang, Yuanzhi Zhu, Lianwen Jin +5
Text recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. How…
Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection
Yuliang Liu, Tong He, Hao Chen +5
Multi-orientation scene text detection has recently gained significant research attention. Previous methods directly predict words or text lines, typically by using quadrilateral s…
ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT
Yipeng Sun, Zihan Ni, Chee-Kheng Chng +9
Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavi…
ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)
Chee-Kheng Chng, Yuliang Liu, Yipeng Sun +11
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 recogn…
Adaptive Embedding Gate for Attention-Based Scene Text Recognition
Xiaoxue Chen, Tianwei Wang, Yuanzhi Zhu +2
Scene text recognition has attracted particular research interest because it is a very challenging problem and has various applications. The most cutting-edge methods are attention…
Tightness-aware Evaluation Protocol for Scene Text Detection
Yuliang Liu, Lianwen Jin, Zecheng Xie +3
Evaluation protocols play key role in the developmental progress of text detection methods. There are strict requirements to ensure that the evaluation methods are fair, objective…
A Multi-Object Rectified Attention Network for Scene Text Recognition
Canjie Luo, Lianwen Jin, Zenghui Sun
Irregular text is widely used. However, it is considerably difficult to recognize because of its various shapes and distorted patterns. In this paper, we thus propose a multi-objec…
Feature Enhancement Network: A Refined Scene Text Detector
Sheng Zhang, Yuliang Liu, Lianwen Jin +1
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. Retrospectively,…