From 594a5727c737511f4a72fcb856b7ef4a15aaef20 Mon Sep 17 00:00:00 2001 From: qywh2023 <134821122+qywh2023@users.noreply.github.com> Date: Fri, 20 Jun 2025 21:03:01 +0800 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2a87f67..5d8467b 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Vis > **OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models**
> Yuliang Liu, Zhang Li, Mingxin Huang, Biao Yang, Wenwen Yu, Chunyuan Li, Xucheng Yin, Cheng-lin Liu, Lianwen Jin, Xiang Bai
[![arXiv](https://img.shields.io/badge/Arxiv-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2305.07895) -[![Dataset](https://img.shields.io/badge/Dataset-Available-lightblue)](https://github.com/qywh2023/OCRbench/blob/main/OCRBench/README.md) +[![Dataset](https://img.shields.io/badge/Dataset-Available-green)](https://github.com/qywh2023/OCRbench/blob/main/OCRBench/README.md) **OCRBench** is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It comprises five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, and all the answers undergo manual verification and correction to ensure a more precise evaluation. More details can be found in [OCRBench README](./OCRBench/README.md).