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- OCRBench: on the hidden mystery of OCR in large multimodal models574
A comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks, revealing both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression recognition.
- MORAN: A Multi-Object Rectified Attention Network for scene text recognition509
A multi-object rectified attention network (MORAN) for general scene text recognition that can read both regular and irregular scene text and achieves state-of-the-art performance.
- ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network457
For the first time, a novel BezierAlign layer is designed for extracting accurate convolution features of a text instance with arbitrary shapes, significantly improving the precision compared with previous methods and introducing negligible computation overhead.
- Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection351
A new Convolutional Neural Networks (CNNs) based method, named Deep Matching Prior Network (DMPNet), to detect text with tighter quadrangle, which has better overall performance than L2 loss and smooth L1 loss in terms of robustness and stability.
- Curved scene text detection via transverse and longitudinal sequence connection318
This work constructed a curved text dataset called CTW1500, and used it to formulate a polygon-based curved text detector that can detect curved text without using an empirical combination, with the seamless integration of recurrent transverse and longitudinal offset connection.
- Fourier Contour Embedding for Arbitrary-Shaped Text Detection311
Extensive experiments demonstrate that FCE is accurate and robust to fit contours of scene texts even with highly-curved shapes, and also validate the effectiveness and the good generalization of FCENet for arbitrary-shaped text detection.
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- High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps309
It has been shown that with the proper incorporation with traditional directional feature maps, the proposed single and ensemble HCCR-GoogLeNet models achieve new state of the art recognition accuracy of 96.35% and 96.74%, respectively, outperforming previous best result with significant gap.
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- Decoupled Attention Network for Text Recognition299
A decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results, and achieves state-of-the-art performance on multiple text recognition tasks, including offline handwritten text recognition and regular/irregular scene text recognition.
- ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT272
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) sceneText recognition, and iii) scenetext spotting.
- SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction222
It is argued that FBP is a multi-paradigm computation problem, and a new diverse benchmark dataset, called SCUT-FBP5500, is proposed to achieve multi-Paradigm facial beauty prediction.
- Scale-Aware Modulation Meet Transformer208
A new vision Transformer that can handle various downstream tasks efficiently by combining the convolutional network and vision Transformer is presented, and an Evolutionary Hybrid Network (EHN) is proposed, which can effectively simulate the shift from capturing local to global dependencies as the network becomes deeper, resulting in superior performance.
- ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling - RRC-LSVT207
This competition aims to explore the abilities of state-of-the-art methods to detect and recognize text instances from large-scale street view images, closing gaps between research benchmarks and real applications.
- LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding201
Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure.
- Text Recognition in the Wild201
This literature review attempts to present an entire picture of the field of scene text recognition, which provides a comprehensive reference for people entering this field and could be helpful in inspiring future research.
- Hierarchical Deep Reinforcement Learning for Continuous Action Control197
The results show that the proposed algorithm can learn both high performance basic skills and compound skills through the same learning process, and the compound skills learned outperform those learned by a discrete action space deep reinforcement learning algorithm.
- ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting189
This work adaptively fit arbitrarily-shaped text by a parameterized Bezier curve, which, compared with segmentation-based methods, can not only provide structured output but also controllable representation, which can be valuable for real-time applications.
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- Person Re-Identification by Regularized Smoothing KISS Metric Learning174
This paper presents regularized smoothing KISS metric learning (RS-KISS) by seamlessly integrating smoothing and regularization techniques for robustly estimating covariance matrices and introduces incremental learning to RS-K ISS.
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- SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition167
A new end-to-end scene text spotting framework termed SwinTextSpotter is proposed, using a transformer encoder with dynamic head as the detector and a novel Recognition Conversion mechanism to explicitly guide text localization through recognition loss.
- Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition165
A nine-layer CNN for HCCR consisting of 3755 classes is designed, and an algorithm is devised that can reduce the networks computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0.21% drop in accuracy.
- SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception148
The results of the experiments indicate that the SCUT-FBP dataset provides a reliable benchmark for facial beauty perception.
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 2026-10-14. 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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