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556 lines
22 KiB
Markdown
556 lines
22 KiB
Markdown
<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
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> [English](./README.en.md) · [原始项目](https://github.com/allenai/olmocr) · [上游 README](https://github.com/allenai/olmocr/blob/HEAD/README.md)
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> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
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<div align="center">
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<img width="350" alt="olmocr-2-full@2x" src="https://github.com/user-attachments/assets/24f1b596-4059-46f1-8130-5d72dcc0b02e" />
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<hr/>
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</div>
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<p align="center">
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<a href="https://github.com/allenai/OLMo/blob/main/LICENSE">
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<img alt="GitHub License" src="https://img.shields.io/github/license/allenai/OLMo">
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</a>
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<a href="https://github.com/allenai/olmocr/releases">
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<img alt="GitHub release" src="https://img.shields.io/github/release/allenai/olmocr.svg">
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</a>
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<a href="https://arxiv.org/abs/2502.18443">
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<img alt="Tech Report v1" src="https://img.shields.io/badge/Paper_v1-olmOCR-blue">
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</a>
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<a href="https://arxiv.org/abs/2510.19817">
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<img alt="Tech Report v2" src="https://img.shields.io/badge/Paper_v2-olmOCR-blue">
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</a>
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<a href="https://olmocr.allenai.org">
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<img alt="Demo" src="https://img.shields.io/badge/Ai2-Demo-F0529C">
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</a>
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<a href="https://discord.gg/sZq3jTNVNG">
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<img alt="Discord" src="https://img.shields.io/badge/Discord%20-%20blue?style=flat&logo=discord&label=Ai2&color=%235B65E9">
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</a>
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</p>
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一款将 PDF 及其他基于图像的文档格式转换为干净、可读纯文本格式的工具包。
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试用在线演示：[https://olmocr.allenai.org/](https://olmocr.allenai.org/)
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功能特性：
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- 将基于 PDF、PNG 和 JPEG 的文档转换为干净的 Markdown
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- 支持公式、表格、手写体及复杂排版
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- 自动移除页眉和页脚
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- 即使存在插图、多栏布局和嵌入内容，也能按自然阅读顺序转换为文本
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- 高效，每转换一百万页成本低于 200 美元
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- （基于 70 亿参数 VLM（Vision Language Model，视觉语言模型），因此需要 GPU）
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### 新闻
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- 2025 年 10 月 21 日 - v0.4.0 - [新模型发布](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8), 通过合成数据将 olmOCR-bench 分数提升约 4 分，并引入 RL（Reinforcement Learning，强化学习）训练。
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- 2025 年 8 月 13 日 - v0.3.0 - [新模型发布](https://huggingface.co/allenai/olmOCR-7B-0825-FP8), 修复了自动旋转检测及空白文档上的幻觉问题。
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- 2025 年 7 月 24 日 - v0.2.1 - [新模型发布](https://huggingface.co/allenai/olmOCR-7B-0725-FP8), 在 [olmOCR-Bench](https://github.com/allenai/olmocr/tree/main/olmocr/bench), 上得分提高 3 分，同时由于默认采用 FP8，运行速度显著加快，且每份文档所需重试次数大幅减少。
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- 2025 年 7 月 23 日 - v0.2.0 - 全新整理的 [训练代码](https://github.com/allenai/olmocr/tree/main/olmocr/train), 使自行训练 olmOCR 模型变得更加简单。
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- 2025 年 6 月 17 日 - v0.1.75 - 推理流水线从 sglang 切换为基于 vLLM 的实现，Docker 镜像更新至 CUDA 12.8。
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- 2025 年 5 月 23 日 - v0.1.70 - 官方 Docker 支持及镜像现已可用！[查看 Docker 用法](#using-docker)
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- 2025 年 5 月 19 日 - v0.1.68 - [olmOCR-Bench](https://github.com/allenai/olmocr/tree/main/olmocr/bench) 发布，得分 77.4。发布版本因提示词相关 bug 修复，olmOCR 流水线性能提升 2 分。
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- 2025 年 3 月 17 日 - v0.1.60 - 通过改进采样温度选择提升性能。
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- 2025 年 2 月 25 日 - v0.1.58 - 首次公开发布及演示。
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### 基准测试
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[**olmOCR-Bench**](https://github.com/allenai/olmocr/tree/main/olmocr/bench):
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我们还提供一套全面的基准测试套件，涵盖 1,400 份文档中的 7,000 多个测试用例，用于衡量 OCR 系统的性能。
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<table>
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<thead>
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<tr>
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<th></th>
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<th>ArXiv</th>
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<th>Old<br>scans<br>math</th>
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<th>Tables</th>
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<th>Old<br>scans</th>
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<th>Headers<br>&<br>footers</th>
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<th>Multi<br>column</th>
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<th>Long<br>tiny<br>text</th>
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<th>Base</th>
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<th>Overall</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Mistral OCR API</td>
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<td>77.2</td>
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<td>67.5</td>
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<td>60.6</td>
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<td>29.3</td>
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<td>93.6</td>
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<td>71.3</td>
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<td>77.1</td>
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<td>99.4</td>
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<td>72.0±1.1</td>
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</tr>
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<tr>
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<td>Marker 1.10.1</td>
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<td>83.8</td>
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<td>66.8</td>
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<td>72.9</td>
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<td>33.5</td>
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<td>86.6</td>
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<td>80.0</td>
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<td>85.7</td>
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<td>99.3</td>
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<td>76.1±1.1</td>
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</tr>
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<tr>
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<td>MinerU 2.5.4*</td>
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<td>76.6</td>
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<td>54.6</td>
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<td>84.9</td>
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<td>33.7</td>
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<td>96.6</td>
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<td>78.2</td>
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<td>83.5</td>
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<td>93.7</td>
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<td>75.2±1.1</td>
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</tr>
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<tr>
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<td>DeepSeek-OCR</td>
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<td>77.2</td>
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<td>73.6</td>
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<td>80.2</td>
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<td>33.3</td>
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<td>96.1</td>
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<td>66.4</td>
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<td>79.4</td>
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<td>99.8</td>
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<td>75.7±1.0</td>
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</tr>
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<tr>
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<td>Nanonets-OCR2-3B</td>
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<td>75.4</td>
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<td>46.1</td>
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<td>86.8</td>
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<td>40.9</td>
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<td>32.1</td>
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<td>81.9</td>
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<td>93.0</td>
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<td>99.6</td>
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<td>69.5±1.1</td>
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</tr>
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<tr>
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<td>PaddleOCR-VL*</td>
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<td>85.7</td>
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<td>71.0</td>
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<td>84.1</td>
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<td>37.8</td>
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<td>97.0</td>
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<td>79.9</td>
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<td>85.7</td>
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<td>98.5</td>
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<td>80.0±1.0</td>
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</tr>
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<tr>
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<td>Infinity-Parser 7B*</td>
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<td>84.4</td>
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<td>83.8</td>
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<td>85.0</td>
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<td>47.9</td>
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<td>88.7</td>
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<td>84.2</td>
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<td>86.4</td>
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<td>99.8</td>
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<td>82.5±?</td>
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</tr>
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<tr>
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<td>Chandra OCR 0.1.0*</td>
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<td>82.2</td>
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<td>80.3</td>
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<td>88.0</td>
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<td>50.4</td>
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<td>90.8</td>
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<td>81.2</td>
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<td>92.3</td>
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<td>99.9</td>
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<td>83.1±0.9</td>
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</tr>
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<tr>
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<td colspan="10"><hr></td>
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</tr>
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<tr>
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<td><strong>olmOCR v0.4.0</strong></td>
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<td>83.0</td>
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<td>82.3</td>
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<td>84.9</td>
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<td>47.7</td>
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<td>96.1</td>
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<td>83.7</td>
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<td>81.9</td>
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<td>99.7</td>
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<td>82.4±1.1</td>
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</tr>
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</tbody>
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</table>
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### 安装
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#### 系统依赖
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你需要安装 poppler-utils 以及用于渲染 PDF 图像的额外字体。
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安装依赖（Ubuntu/Debian）：
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```bash
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sudo apt-get update
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sudo apt-get install poppler-utils ttf-mscorefonts-installer msttcorefonts fonts-crosextra-caladea fonts-crosextra-carlito gsfonts lcdf-typetools
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```
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#### Python 安装
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设置 conda 环境并安装 olmocr。运行 olmOCR 的依赖项在现有 Python 环境中较难安装，因此请务必创建一个干净的 Python 环境进行安装。
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```bash
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conda create -n olmocr python=3.11
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conda activate olmocr
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```
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选择与你的使用场景匹配的安装选项：
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**选项 1：远程推理（轻量）**
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如果你计划配合 `--server` 标志使用远程 vLLM 服务器，请安装基础包：
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```bash
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pip install olmocr
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```
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这可避免安装 PyTorch（约 2GB+）等重型 GPU 依赖。
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**选项 2：本地 GPU 推理**
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要求：
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- 较新的 NVIDIA GPU（已在 RTX 4090、L40S、A100、H100 上测试），至少 12 GB GPU 显存
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- 30GB 可用磁盘空间
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使用自有 GPU 运行推理：
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```bash
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pip install olmocr[gpu] --extra-index-url https://download.pytorch.org/whl/cu128
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# Recommended: Install flash infer for faster inference on GPU
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pip install https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.5%2Bcu128torch2.7-cp38-abi3-linux_x86_64.whl
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```
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**选项 3：Beaker 集群执行**
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用于向 Beaker 集群提交作业，并配合 `--beaker` 标志：
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```bash
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pip install olmocr[beaker]
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```
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**选项 4：基准测试套件**
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用于运行 olmOCR 基准测试套件：
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```bash
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pip install olmocr[bench]
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```
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**组合安装**
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你可以组合多个选项：
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```bash
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# GPU + Beaker support
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pip install olmocr[gpu,beaker] --extra-index-url https://download.pytorch.org/whl/cu128
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# GPU + Benchmark support
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pip install olmocr[gpu,bench] --extra-index-url https://download.pytorch.org/whl/cu128
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```
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**故障排查**
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如果遇到与 `too many open files` 相关的错误，请更新 ulimit：
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```bash
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ulimit -n 65536
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```
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### 使用示例
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如需快速测试，可尝试 [web demo](https://olmocr.allen.ai/).
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**转换单个 PDF（本地 GPU）：**
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```bash
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# Download a sample PDF
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curl -o olmocr-sample.pdf https://olmocr.allenai.org/papers/olmocr_3pg_sample.pdf
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# Convert it to markdown
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olmocr ./localworkspace --markdown --pdfs olmocr-sample.pdf
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```
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**转换图像文件：**
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```bash
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olmocr ./localworkspace --markdown --pdfs random_page.png
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```
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**转换多个 PDF：**
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```bash
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olmocr ./localworkspace --markdown --pdfs tests/gnarly_pdfs/*.pdf
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```
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**使用远程推理服务器：**
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```bash
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olmocr ./localworkspace --server http://remote-server:8000/v1 --model allenai/olmOCR-2-7B-1025-FP8 --markdown --pdfs *.pdf
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```
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配合 `--markdown` 标志，结果将以 markdown 文件形式保存在 `./localworkspace/markdown/` 内。
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> **注意：** 如果你愿意，也可以使用 `python -m olmocr.pipeline` 代替 `olmocr`。
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#### 查看结果
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随后，`./localworkspace/` 工作区文件夹将同时包含 [Dolma](https://github.com/allenai/dolma) 和 markdown 文件（若使用 `--markdown`）。
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```bash
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cat localworkspace/markdown/olmocr-sample.md
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```
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```
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olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
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...
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```
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### 使用推理提供商或外部服务器
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如果你已在其他地方运行 vLLM 服务器（或任何实现 OpenAI API 的推理平台），可以让 olmOCR 指向它，而无需在本地启动实例。
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**远程推理安装：**
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```bash
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# Lightweight installation - no GPU dependencies needed
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pip install olmocr
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```
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**使用外部服务器：**
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```bash
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# Use external vLLM server instead of local one
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olmocr ./localworkspace --server http://remote-server:8000/v1 --model allenai/olmOCR-2-7B-1025-FP8 --markdown --pdfs tests/gnarly_pdfs/*.pdf
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```
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vLLM 中提供的模型名称必须与 `--model` 中提供的值一致。
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**vLLM 服务器启动示例：**
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```bash
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vllm serve allenai/olmOCR-2-7B-1025-FP8 --max-model-len 16384
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```
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#### 已验证的外部提供商
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我们已在以下外部模型提供商上测试 `olmOCR-2-7B-1025-FP8`，并确认其可用
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| | $/1M Input tokens | $/1M Output tokens | Example Command |
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|-----------------------------------------------------------------------------|-------------------|--------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [Cirrascale](https://ai2endpoints.cirrascale.ai/models/overview) | $0.07 | $0.15 | `olmocr ./workspace --server https://ai2endpoints.cirrascale.ai/api --api_key sk-XXXXXXX --workers 1 --max_concurrent_requests 20 --model olmOCR-2-7B-1025 --pdfs tests/gnarly_pdfs/*.pdf` |
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| [DeepInfra](https://deepinfra.com/) | $0.09 | $0.19 | `olmocr ./workspace --server https://api.deepinfra.com/v1/openai --api_key DfXXXXXXX --workers 1 --max_concurrent_requests 20 --model allenai/olmOCR-2-7B-1025 --pdfs tests/gnarly_pdfs/*.pdf` |
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| [Parasail](https://www.saas.parasail.io/serverless?name=olmocr-7b-1025-fp8) | $0.10 | $0.20 | `olmocr ./workspace --server https://api.parasail.io/v1 --api_key psk-XXXXX --workers 1 --max_concurrent_requests 20 --model allenai/olmOCR-2-7B-1025 --pdfs tests/gnarly_pdfs/*.pdf` |
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参数说明
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- `--server`：定义 OpenAI 兼容端点，例如 `https://api.deepinfra.com/v1/openai`
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- `--api_key`：你的 API 密钥，通过 Authorization Bearer HTTP 标头传入
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- `--max_concurrent_requests`：同时发往推理提供商的在途请求最大并发数
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- `--workers`：一次处理的最大页面组数量。你可能希望将其设为 `1`，以便在处理下一组之前先完成当前一组。
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- `--pages_per_group`：你可能希望每组包含更少的页数，因为许多外部提供商的并发请求上限较低
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- `--model`：模型标识符，例如 `allenai/olmOCR-2-7B-1025`；不同提供商的名称各不相同，若在本地运行，可使用 `olmocr`
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- 其他参数与本地推理时的用法相同
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### 多节点 / 集群用法
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如果你想使用多个并行运行的节点将数百万份 PDF 进行转换，olmOCR 支持
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从 AWS S3 读取 PDF，并使用 AWS S3 输出存储桶协调任务。
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**启动第一个工作节点：**
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```bash
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olmocr s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf
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```
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这会在你的 AWS 存储桶中建立一个简单的工作队列，并开始转换 PDF。
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**在后续工作节点上：**
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```bash
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olmocr s3://my_s3_bucket/pdfworkspaces/exampleworkspace
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```
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||
|
||
它们会自动从同一工作区队列中获取任务。
|
||
|
||
#### 使用 Beaker 进行集群执行
|
||
|
||
如果你在 Ai2，并希望借助 [beaker](https://www.beaker.org), 高效地将数百万份 PDF 线性化，请安装带 Beaker 支持的版本：
|
||
|
||
```bash
|
||
pip install olmocr[gpu,beaker] --extra-index-url https://download.pytorch.org/whl/cu128
|
||
```
|
||
|
||
然后使用 `--beaker` 标志在本地准备工作区，并在集群中启动 N 个 GPU 工作节点：
|
||
|
||
```bash
|
||
olmocr s3://my_s3_bucket/pdfworkspaces/exampleworkspace --pdfs s3://my_s3_bucket/jakep/gnarly_pdfs/*.pdf --beaker --beaker_gpus 4
|
||
```
|
||
|
||
|
||
### 使用 Docker
|
||
|
||
拉取 Docker 镜像（体积较大，包含模型，约 30GB）：
|
||
```bash
|
||
docker pull alleninstituteforai/olmocr:latest-with-model
|
||
```
|
||
|
||
对于希望自行管理模型下载的高级用户，我们还提供不含模型的基础镜像：
|
||
```bash
|
||
docker pull alleninstituteforai/olmocr:latest
|
||
```
|
||
|
||
#### 快速开始 - 处理 PDF
|
||
|
||
处理当前目录中的单个 PDF：
|
||
```bash
|
||
docker run --gpus all \
|
||
-v $(pwd):/workspace \
|
||
alleninstituteforai/olmocr:latest-with-model \
|
||
-c "olmocr /workspace/output --markdown --pdfs /workspace/sample.pdf"
|
||
```
|
||
|
||
处理多个 PDF：
|
||
```bash
|
||
docker run --gpus all \
|
||
-v /path/to/pdfs:/input \
|
||
-v /path/to/output:/output \
|
||
alleninstituteforai/olmocr:latest-with-model \
|
||
-c "olmocr /output --markdown --pdfs /input/*.pdf"
|
||
```
|
||
|
||
#### 交互模式
|
||
|
||
以交互方式运行容器，便于探索和调试：
|
||
```bash
|
||
docker run -it --gpus all alleninstituteforai/olmocr:latest-with-model
|
||
```
|
||
|
||
> 访问我们在 [Docker Hub](https://hub.docker.com/r/alleninstituteforai/olmocr) 上的 Docker 仓库以了解更多信息。
|
||
|
||
### 完整文档
|
||
|
||
要查看所有可用选项：
|
||
```bash
|
||
olmocr --help
|
||
usage: pipeline.py [-h] [--pdfs [PDFS ...]] [--model MODEL] [--workspace_profile WORKSPACE_PROFILE] [--pdf_profile PDF_PROFILE] [--pages_per_group PAGES_PER_GROUP] [--max_page_retries MAX_PAGE_RETRIES] [--max_page_error_rate MAX_PAGE_ERROR_RATE] [--workers WORKERS]
|
||
[--apply_filter] [--stats] [--markdown] [--target_longest_image_dim TARGET_LONGEST_IMAGE_DIM] [--target_anchor_text_len TARGET_ANCHOR_TEXT_LEN] [--guided_decoding] [--gpu-memory-utilization GPU_MEMORY_UTILIZATION] [--max_model_len MAX_MODEL_LEN]
|
||
[--tensor-parallel-size TENSOR_PARALLEL_SIZE] [--data-parallel-size DATA_PARALLEL_SIZE] [--port PORT] [--server SERVER] [--beaker] [--beaker_workspace BEAKER_WORKSPACE] [--beaker_cluster BEAKER_CLUSTER] [--beaker_gpus BEAKER_GPUS] [--beaker_priority BEAKER_PRIORITY]
|
||
workspace
|
||
|
||
Manager for running millions of PDFs through a batch inference pipeline
|
||
|
||
positional arguments:
|
||
workspace The filesystem path where work will be stored, can be a local folder, or an s3 path if coordinating work with many workers, s3://bucket/prefix/
|
||
|
||
options:
|
||
-h, --help show this help message and exit
|
||
--pdfs [PDFS ...] Path to add pdfs stored in s3 to the workspace, can be a glob path s3://bucket/prefix/*.pdf or path to file containing list of pdf paths
|
||
--model MODEL Path where the model is located, allenai/olmOCR-7B-0725-FP8 is the default, can be local, s3, or hugging face.
|
||
--workspace_profile WORKSPACE_PROFILE
|
||
S3 configuration profile for accessing the workspace
|
||
--pdf_profile PDF_PROFILE
|
||
S3 configuration profile for accessing the raw pdf documents
|
||
--pages_per_group PAGES_PER_GROUP
|
||
Aiming for this many pdf pages per work item group
|
||
--max_page_retries MAX_PAGE_RETRIES
|
||
Max number of times we will retry rendering a page
|
||
--max_page_error_rate MAX_PAGE_ERROR_RATE
|
||
Rate of allowable failed pages in a document, 1/250 by default
|
||
--workers WORKERS Number of workers to run at a time
|
||
--apply_filter Apply basic filtering to English pdfs which are not forms, and not likely seo spam
|
||
--stats Instead of running any job, reports some statistics about the current workspace
|
||
--markdown Also write natural text to markdown files preserving the folder structure of the input pdfs
|
||
--target_longest_image_dim TARGET_LONGEST_IMAGE_DIM
|
||
Dimension on longest side to use for rendering the pdf pages
|
||
--target_anchor_text_len TARGET_ANCHOR_TEXT_LEN
|
||
Maximum amount of anchor text to use (characters), not used for new models
|
||
--guided_decoding Enable guided decoding for model YAML type outputs
|
||
|
||
VLLM arguments:
|
||
--gpu-memory-utilization GPU_MEMORY_UTILIZATION
|
||
Fraction of VRAM vLLM may pre-allocate for KV-cache (passed through to vllm serve).
|
||
--max_model_len MAX_MODEL_LEN
|
||
Upper bound (tokens) vLLM will allocate KV-cache for, lower if VLLM won't start
|
||
--tensor-parallel-size TENSOR_PARALLEL_SIZE, -tp TENSOR_PARALLEL_SIZE
|
||
Tensor parallel size for vLLM
|
||
--data-parallel-size DATA_PARALLEL_SIZE, -dp DATA_PARALLEL_SIZE
|
||
Data parallel size for vLLM
|
||
--port PORT Port to use for the VLLM server
|
||
--server SERVER URL of external vLLM (or other compatible provider)
|
||
server (e.g., http://hostname:port). If provided,
|
||
skips spawning local vLLM instance
|
||
|
||
beaker/cluster execution:
|
||
--beaker Submit this job to beaker instead of running locally
|
||
--beaker_workspace BEAKER_WORKSPACE
|
||
Beaker workspace to submit to
|
||
--beaker_cluster BEAKER_CLUSTER
|
||
Beaker clusters you want to run on
|
||
--beaker_gpus BEAKER_GPUS
|
||
Number of gpu replicas to run
|
||
--beaker_priority BEAKER_PRIORITY
|
||
Beaker priority level for the job
|
||
```
|
||
|
||
## 代码概览
|
||
|
||
其中有一些很好的可复用代码片段，也许对你自己的项目有帮助：
|
||
- 一种使用 ChatGPT 4o 获得高质量自然语言文本解析的提示策略（prompting strategy）- [buildsilver.py](https://github.com/allenai/olmocr/blob/main/olmocr/data/buildsilver.py)
|
||
- 按语言进行基础过滤并去除 SEO 垃圾内容 - [filter.py](https://github.com/allenai/olmocr/blob/main/olmocr/filter/filter.py)
|
||
- Qwen2.5-VL 的 SFT 微调代码 - [train.py](https://github.com/allenai/olmocr/blob/main/olmocr/train/train.py)
|
||
- GRPO 强化学习（RL）训练器 - [grpo_train.py](https://github.com/allenai/olmocr/blob/main/olmocr/train/grpo_train.py)
|
||
- 合成数据生成 - [mine_html_templates.py](https://github.com/allenai/olmocr/blob/main/olmocr/synth/mine_html_templates.py)
|
||
- 使用 VLLM 通过微调模型处理数百万份 PDF - [pipeline.py](https://github.com/allenai/olmocr/blob/main/olmocr/pipeline.py)
|
||
- 查看由 PDF 生成的 [Dolma 文档](https://github.com/allenai/dolma) - [dolmaviewer.py](https://github.com/allenai/olmocr/blob/main/olmocr/viewer/dolmaviewer.py)
|
||
|
||
|
||
|
||
## 团队
|
||
|
||
<!-- start team -->
|
||
|
||
**olmOCR** 由 AllenNLP 团队开发并维护，得到 [艾伦人工智能研究所（Allen Institute for Artificial Intelligence，AI2）](https://allenai.org/). 的支持
|
||
AI2 是一家非营利研究机构，使命是通过高影响力的 AI 研究与工程为人类作出贡献。
|
||
若要了解具体有哪些人为本代码库作出贡献，请参阅[我们的贡献者](https://github.com/allenai/olmocr/graphs/contributors) 页面。
|
||
|
||
<!-- end team -->
|
||
|
||
## 许可证
|
||
|
||
<!-- start license -->
|
||
|
||
**olmOCR** 采用 [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). 许可证
|
||
完整许可证副本可在 [GitHub](https://github.com/allenai/olmocr/blob/main/LICENSE). 上找到
|
||
|
||
<!-- end license -->
|
||
|
||
## 引用
|
||
|
||
引用 olmOCR v1 与 OlmOCR-bench：
|
||
```bibtex
|
||
@misc{olmocrbench,
|
||
title={{olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models}},
|
||
author={Jake Poznanski and Jon Borchardt and Jason Dunkelberger and Regan Huff and Daniel Lin and Aman Rangapur and Christopher Wilhelm and Kyle Lo and Luca Soldaini},
|
||
year={2025},
|
||
eprint={2502.18443},
|
||
archivePrefix={arXiv},
|
||
primaryClass={cs.CL},
|
||
url={https://arxiv.org/abs/2502.18443},
|
||
}
|
||
```
|
||
|
||
引用 olmOCR v2 Unit Testing Rewards with RL：
|
||
```bibtex
|
||
@misc{olmocr2,
|
||
title={olmOCR 2: Unit Test Rewards for Document OCR},
|
||
author={Jake Poznanski and Luca Soldaini and Kyle Lo},
|
||
year={2025},
|
||
eprint={2510.19817},
|
||
archivePrefix={arXiv},
|
||
primaryClass={cs.CV},
|
||
url={https://arxiv.org/abs/2510.19817},
|
||
}
|
||
```
|