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Works79 from public data
- An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition3,175
A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed, which generates an effective yet much smaller model, which is more practical for real-world application scenarios.
- EAST: An Efficient and Accurate Scene Text Detector1,850
This work proposes a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes, and significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency.
- 906
- Real-Time Scene Text Detection with Differentiable Binarization891
This paper proposes a module named Differentiable Binarization (DB), which can perform the binarization process in a segmentation network, and validate the performance improvements of DB on five benchmark datasets, which consistently achieves state-of-the-art results, in terms of both detection accuracy and speed.
- 879
- Robust Scene Text Recognition with Automatic Rectification693
RARE (Robust text recognizer with Automatic REctification), a recognition model that is robust to irregular text, which is end-to-end trainable, requiring only images and associated text labels, making it convenient to train and deploy the model in practical systems.
- Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes650
The recognition module of the Mask TextSpotter method is investigated separately, which significantly outperforms state-of-the-art methods on both regular and irregular text datasets for scene text recognition.
- TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes636
A more flexible representation for scene text is proposed, termed as TextSnake, which is able to effectively represent text instances in horizontal, oriented and curved forms and outperforms the baseline on Total-Text by more than 40% in F-measure.
- Multi-oriented Text Detection with Fully Convolutional Networks596
A novel approach for text detection in natural images that consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, I CDAR2015 and ICDAR2013.
- Scene Text Detection and Recognition: The Deep Learning Era527
This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era.
- Real-Time Scene Text Detection With Differentiable Binarization and Adaptive Scale Fusion431
A Differentiable Binarization (DB) module that integrates the binarization process, one of the most important steps in the post-processing procedure, into a segmentation network is proposed and an efficient Adaptive Scale Fusion (ASF) module is proposed to improve the scale robustness by fusing features of different scales adaptively.
- Scene text detection and recognition: recent advances and future trends402
This literature review can serve as a good reference for researchers in the areas of scene text detection and recognition and identify state-of-the-art algorithms, and predict potential research directions in the future.
- Multi-oriented Scene Text Detection via Corner Localization and Region Segmentation389
This paper proposes to detect scene text by localizing corner points of text bounding boxes and segmenting text regions in relative positions and achieves better or comparable results in both accuracy and efficiency.
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- ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)256
This report introduces RCTW, a new competition that focuses on Chinese text reading with a large-scale dataset with over 12,000 annotated images and calls for more future research on the Chinese textReading problem.
- Scene Text Recognition from Two-Dimensional Perspective251
A simple yet effective model, called Character Attention Fully Convolutional Network (CA-FCN), is devised for recognizing the text of arbitrary shapes with a semantic segmentation network, where an attention mechanism for characters is adopted.
- Scene Text Detection with Supervised Pyramid Context Network238
A supervised pyramid context network (SPCNET) is proposed to precisely locate text regions while suppressing false positives in scene text detection, based on Feature Pyramid Network (FPN) and instance segmentation.
- Deep Learning Representation using Autoencoder for 3D Shape Retrieval187
This work projects 3D shapes into 2D space and uses autoencoder for feature learning on the 2D images and shows the proposed deep learning feature is complementary to conventional local image descriptors, which can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.
- 185
- LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding163
Experiments show that the proposed LayoutLLM significantly outperforms existing methods that adopt open-source 7B LLMs/MLLMs for document understanding, and brings a certain degree of interpretability, which could facilitate manual inspection and correction.
- TextScanner: Reading Characters in Order for Robust Scene Text Recognition162
TextScanner bears three characteristics: it belongs to the semantic segmentation family, as it generates pixel-wise, multi-channel segmentation maps for character class, position and order, and also adopts RNN for context modeling.
- Symmetry-Constrained Rectification Network for Scene Text Recognition153
A Symmetry-constrained Rectification Network (ScRN) based on local attributes of text instances, such as center line, scale and orientation, which enables ScRN to generate better rectification results than existing methods and thus lead to higher recognition accuracy.
- Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis152
This work treats the calligraphy synthesis problem as an image-to-image translation problem and proposes a deep neural network based model which can generate calligraphY images from standard font images directly.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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