From 7283345a9ee7d0b7002dd80601ddc1f9bcbfc2bd Mon Sep 17 00:00:00 2001 From: 99Franklin <358622371@qq.com> Date: Fri, 3 Jan 2025 15:10:20 +0800 Subject: [PATCH] add paper link --- OCRBench_v2/README.md | 14 ++++++++++++-- README.md | 10 ++++++++++ 2 files changed, 22 insertions(+), 2 deletions(-) diff --git a/OCRBench_v2/README.md b/OCRBench_v2/README.md index cfb5c78..31ecb9b 100644 --- a/OCRBench_v2/README.md +++ b/OCRBench_v2/README.md @@ -2,7 +2,7 @@ > Scoring the Optical Character Recognition (OCR) capabilities of Large Multimodal Models (LMMs) has witnessed growing interest recently. Existing benchmarks have highlighted the impressive performance of LMMs in text recognition; however, their abilities in certain challenging tasks, such as text localization, handwritten content extraction, and logical reasoning, remain underexplored. To bridge this gap, we introduce OCRBench v2, a large-scale bilingual text-centric benchmark with currently the most comprehensive set of tasks (4X more tasks than the previous multi-scene benchmark OCRBench), the widest coverage of scenarios (31 diverse scenarios including street scene, receipt, formula, diagram, and so on), and thorough evaluation metrics, with a total of 10,000 human-verified question-answering pairs and a high proportion of difficult samples. After carefully benchmarking state-of-the-art LMMs on OCRBench v2, we find that 36 out of 38 LMMs score below 50 (100 in total) and suffer from five-type limitations, including less frequently encountered text recognition, fine-grained perception, layout perception, complex element parsing, and logical reasoning. -**[Project Page](https://github.com/Yuliang-Liu/MultimodalOCR)** | **Paper(Coming soon)** | **[OCRBench v2 Leaderboard](https://huggingface.co/spaces/ling99/OCRBench-v2-leaderboard)** +**[Project Page](https://github.com/Yuliang-Liu/MultimodalOCR)** | **[Paper](https://arxiv.org/abs/2501.00321)** | **[OCRBench v2 Leaderboard](https://huggingface.co/spaces/ling99/OCRBench-v2-leaderboard)**

@@ -81,4 +81,14 @@ python ./eval_scripts/get_score.py --json_file ./res_folder/internvl2_5_26b.json The data are collected from public datasets and community user contributions. This dataset is for research purposes only and not for commercial use. If you have any copyright concerns, please contact ling_fu@hust.edu.cn. # Citation -Coming soon +```BibTeX +@misc{fu2024ocrbenchv2improvedbenchmark, + title={OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning}, + author={Ling Fu and Biao Yang and Zhebin Kuang and Jiajun Song and Yuzhe Li and Linghao Zhu and Qidi Luo and Xinyu Wang and Hao Lu and Mingxin Huang and Zhang Li and Guozhi Tang and Bin Shan and Chunhui Lin and Qi Liu and Binghong Wu and Hao Feng and Hao Liu and Can Huang and Jingqun Tang and Wei Chen and Lianwen Jin and Yuliang Liu and Xiang Bai}, + year={2024}, + eprint={2501.00321}, + archivePrefix={arXiv}, + primaryClass={cs.CV}, + url={https://arxiv.org/abs/2501.00321}, +} +``` diff --git a/README.md b/README.md index 63e2f2f..9609d3d 100644 --- a/README.md +++ b/README.md @@ -49,6 +49,16 @@ If you wish to refer to the baseline results published here, please use the foll author={Liu, Yuliang and Li, Zhang and Huang, Mingxin and Yang, Biao and Yu, Wenwen and Li, Chunyuan and Yin, Xu-Cheng and Liu, Cheng-Lin and Jin, Lianwen and Bai, Xiang}, year={2024}, month=dec } + +@misc{fu2024ocrbenchv2improvedbenchmark, + title={OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning}, + author={Ling Fu and Biao Yang and Zhebin Kuang and Jiajun Song and Yuzhe Li and Linghao Zhu and Qidi Luo and Xinyu Wang and Hao Lu and Mingxin Huang and Zhang Li and Guozhi Tang and Bin Shan and Chunhui Lin and Qi Liu and Binghong Wu and Hao Feng and Hao Liu and Can Huang and Jingqun Tang and Wei Chen and Lianwen Jin and Yuliang Liu and Xiang Bai}, + year={2024}, + eprint={2501.00321}, + archivePrefix={arXiv}, + primaryClass={cs.CV}, + url={https://arxiv.org/abs/2501.00321}, +} ```