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Found 20 papers, 14 papers with code
PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition
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.
Knowing Where and What: Unified Word Block Pretraining for Document Understanding
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.
Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild
To address this issue, we propose a novel approach called Marior (Margin Removal and \Iterative Content Rectification).
Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context
To address this issue, we propose a Contextual-guided Text Removal Network, termed as CTRNet.
Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator
Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures.
SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization
Motivated by this common sense, we augment one image patch and use its neighboring patch as guidance to recover itself.
SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text
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.
Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter
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
Text Recognition in the Wild: A Survey
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.
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
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.
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering
Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize.
Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild
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.
Decoupled Attention Network for Text Recognition
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
Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection
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
ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT
Robust text reading from street view images provides valuable information for various applications.
ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)
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.
Adaptive Embedding Gate for Attention-Based Scene Text Recognition
Scene text recognition has attracted particular research interest because it is a very challenging problem and has various applications.
Tightness-aware Evaluation Protocol for Scene Text Detection
Evaluation protocols play key role in the developmental progress of text detection methods.
A Multi-Object Rectified Attention Network for Scene Text Recognition
It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text.
Feature Enhancement Network: A Refined Scene Text Detector
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.
