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Claim this profileWorks122 from public data
- 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.
- Monkey: Image Resolution and Text Label are Important Things for Large Multi-Modal Models485
Monkey is introduced to enhance LMM capabilities and surpasses existing LMMs in many tasks like Image Captioning and various Visual Question Answering formats, and in qualitative tests focused on dense text question answering, Monkey has exhibited encouraging results.
- 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.
- An efficient realization of deep learning for traffic data imputation316
This work developed an algorithm for efficient realization of deep learning for traffic data imputation by training the model hierarchically using the full set of data from all vehicle detector stations, and investigated why the deep leaning model works well for traffic Data Imputation by visualizing the features extracted by the first hidden layer.
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- 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.
- TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document222
TextMonkey is presented, a large multimodal model (LMM) tailored for text-centric tasks and sets a new standard on OCRBench, a comprehensive benchmark consisting of 29 OCR-related assessments, with a score of 561, surpassing previous open-sourced large multimodal models for document understanding.
- 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.
- 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.
- 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.
- On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering132
A dataset that takes a step towards addressing the VQA problem in that it contains questions expressed in two languages, and an evaluation process that co-opts a well understood image-based metric to reflect the method’s ability to reason.
- Aggregation Cross-Entropy for Sequence Recognition124
This paper proposes a novel method, aggregation cross-entropy (ACE), for sequence recognition from a brand new perspective, which requires only characters and their numbers in the sequence annotation for supervision, which allows it to advance beyond sequence recognition, e.g., counting problem.
- Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization109
This paper proposes an efficient video decomposition that represents each video as keyframes and temporal motions, then adapted to an LLM using well-designed tokenizers that discretize visual and temporal information as a few tokens, thus enabling unified generative pre-training of videos, images, and text.
- OMNIPARSER: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition107
A universal model, called OmniParser, which can simultaneously handle three typical visually-situated text parsing tasks: text spotting, key information extraction, and table recognition is devised, which achieves state-of-the-art or highly competitive performances on 7 datasets for the three visually-situated text parsing tasks, despite its unified, concise design.
- SPTS v2: Single-Point Scene Text Spotting99
The SPTS v2 can outperform previous state-of-the-art single-point text spotters with fewer parameters while achieving 19× faster inference speed and experiments suggest a potential preference for single- point representation in scene text spotting when compared to other representations.
- Turning a CLIP Model into a Scene Text Detector96
This paper proposes a new method, termed TCM, focusing on Turning the CLIP Model directly for text detection without pretraining process, and achieves promising domain adaptation ability.
- SPTS: Single-Point Text Spotting84
This work demonstrates that training scene text spotting models can be achieved with an extremely low-cost annotation of a single-point for each instance, and shows that the performance is not very sensitive to the positions of the point annotation, meaning that it can be much easier to be annotated or even be automatically generated than the bounding box that requires precise positions.
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- TextSquare: Scaling up Text-Centric Visual Instruction Tuning79
A new approach for creating a massive, high-quality instruction-tuning dataset, Square-10M, which is generated using closed-source MLLMs and considerably surpasses open-source previous state-of-the-art Text-centric MLLMs, and sets a new standard on OCRBench.
- Structured Multimodal Attentions for TextVQA73
This paper proposes an end-to-end structured multimodal attention (SMA) neural network that outperforms the SoTA models on TextVQA dataset and two tasks of ST-VQ a dataset among all models except pre-training based TAP.
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- Feature Enhancement Network: A Refined Scene Text Detector57
A new FEN network with task-specific, low and high level semantic features fusion to improve the performance of text detection and an effective positives mining strategy for efficiently training the network is proposed.
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 2026-10-15. 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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