Baidu拥有发布无限OCR2，一种开源OCR模型，为非常长的文档所建。 3B模式在一次请求中解析了数百页的书籍，备案和报告。 其核心变化是SR-SWA，这是一个很少的注意计划，使逐步解码成本几乎平坦。 麻省理工学院许可下的重量船，代码为GitHub.
TL；数据
- 大小：3.34B参数。 未披露活动参数。 最大位置长度131,072令牌。
- 运行在：1 CUDA GPU在BF16. Baidu在NVIDIA A800-80GB上测试。 BF16重量总计约6.7GB.
- 性能：在两个多页的基准测试 Baidu测试中，相对于其他9种模型(自报)排名第一。
- 最佳：在马拉松道本奇(百页)上总共77.1个。 下一个型号Kimi-K3，得33.7分。
- 最糟糕的是：48.2条关于马拉松道克本奇(马拉松道克本奇)的分级，这是其最低的分数。
什么是无限OCR 2?
无限制 OCR 2 是一个来自 Baidu 的 3B 视觉语言模型，将命令的页面图像转换成结构化的文本。 输出覆盖文本，表格，公式，数字，标题和读取顺序。 它于2026年6月接替无限制-OCR，发布，以进一步推进DeepSeek-OCR.
配置保持DeepSeek-OCR风格布局。 一台SAM VIT-B型加CLIP-L型编码器为12层混合型专家解码器提供素材。 每条标致路线到64位专家中的6位，加上2位共享专家。 DeepSeek-OCR纸将这个解码器描述为DeepSeek3B-MoE-A570M. Baidu没有为第2版声明主动计数。 在多页模式下，每页花费273个图像符号。
SR -SWA是怎么工作的？
全注意力成本随序列长度增长四倍。 在解码过程中，每个新令牌扫描一个更长的KV缓存。 每页有273个图像信使，100页在任何输出前增加27,300个输入信使。
SR-SWA代表Sparse参考滑动窗口关注。 它将文字-视觉注意力压缩为一套固定的工作参考。 Baidu的型号卡给出512个令牌作为示例大小。 配置对齐一个128个托肯的本地窗口，在32个托肯块之上有前16个选择。 16块32个符号符合512块的预算
随着文件的不断增长，这保持了一步一步的时间和记忆几乎不变。 它称结果为 " 无限范围 "，图像编码和总运行时间仍然随页数而上升。
MPDoc Bench和马拉松Doc Bench的得分如何？.
Baidu团队对MPDoc Bench进行测试，每文档有数十页，马拉松Doc Bench则有数百页。 MPDoc Bench-Parse是一个公开的基准测试，共有433份文件和3,246页。 我们找不到一个公开的马拉松Doc Bench释放。
Per Baidu的领导板图，无限OCR 2在MPDoc Bench上得分84.6分。 Qwen3.8-Max跟随78.3，然后Qwen-3.8-Flash-Next77.4. 在马拉松Doc Bench上它得分77.1分，Kimi-K3是下一个33.7分，然后Gemini-3.8-Flash是28.6分。
基准测试之间的掉落是真实的故事。 Qwen3.8-Max在百页文档中从78.3降至22.9. 无限制OCR 2降7.5分。 其前身"无限-OCR"在马拉松道克本奇上得分0.0.
亚分数(MPDoc Bench /马拉松Doc Bench):
- 案文：94.8/89.6
- 公式：89.4/87.9
- 阅读顺序：92.3/85.3
- 表：87.8/74.8
- 标题等级：60.1/48.2
MPDoc Bench上的单度量边很薄。 Qwen3.8-Max在文本上得分94.3分，在公式上得分89.2分。 所有数字都是自报的，只出现在图表图中。
与其他开放的 OCR 模型的比较
每个型号的Hugging Face页的参数计数和许可证。 基准测试从Baidu的图中得分。
你怎么运行无限制 OCR 2?
型号卡片列出用于快速测试的变形金刚以及用于服务的SGLang. 在"变形金刚"中，推论手柄1页带有动态的t，称为gundam模式。 推断1个请求中文档的每页都含有多解析值，因此该模型保持跨页上下文。 装入需要信任 remote code=True，它从repo运行Python代码。
SGLang服务使用来自Baidu的定制车轮，而非上游SGLang. 兹Q 1Z的示例列表每个窗口有10页，并重用上一个窗口的KV尾巴。 较小的windows剪切峰值内存但削弱跨页合并。 Baidu 警告默认偏差设置可以触发462 MiB分配，并耗尽内存。
关键外卖
- 无限制 OCR 2 是来自Baidu的3.34B,MIT许可的OCR型号。
- SR-SWA在固定的512托肯参考集引起注意。
- 马拉松道本奇的得分是77.1，而下一个型号是33.7。
- 头级是弱点，长文件48.2
- 所有基准测试数字都是Baidu自己的。
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Baidu has released Unlimited OCR 2, an open-source OCR model built for very long documents. The 3B model parses books, filings and reports of hundreds of pages in one request. Its core change is SR-SWA, a sparse attention scheme that keeps per-step decoding cost nearly flat. Weights ship under the MIT license, with code on GitHub.
TL;DR
- Size: 3.34B parameters. Active parameters not disclosed. Max position length 131,072 tokens.
- Runs on: 1 CUDA GPU in BF16. Baidu tested on an NVIDIA A800-80GB. BF16 weights total about 6.7 GB.
- Performance: first place on both multi-page benchmarks Baidu tested, against 9 other models (self-reported).
- Best: 77.1 overall on MarathonDocBench (hundreds of pages). The next model, Kimi-K3, scores 33.7.
- Worst: 48.2 on heading hierarchy (TEDS) on MarathonDocBench, its lowest sub-score.
What is Unlimited OCR 2?
Unlimited OCR 2 is a 3B vision-language model from Baidu that turns ordered page images into structured text. Output covers text, tables, formulas, figures, headings and reading order. It succeeds Unlimited-OCR, released in June 2026 to push DeepSeek-OCR further.
The config keeps a DeepSeek-OCR style layout. A SAM ViT-B plus CLIP-L encoder feeds a 12-layer mixture-of-experts decoder. Each token routes to 6 of 64 experts, plus 2 shared experts. The DeepSeek-OCR paper describes this decoder as DeepSeek3B-MoE-A570M. Baidu does not state an active count for version 2. In multi-page mode, each page costs 273 image tokens.
How does SR-SWA work?
Full attention cost grows quadratically with sequence length. During decoding, each new token scans a longer KV cache. At 273 image tokens per page, 100 pages add 27,300 input tokens before any output.
SR-SWA stands for Sparse Reference Sliding Window Attention. It compresses text-visual attention into a fixed set of working references. Baidu’s model card gives 512 tokens as the example size. The config pairs a 128-token local window with top-16 selection over 32-token blocks. That is 16 blocks of 32 tokens, matching the 512-token budget.
This keeps per-step latency and memory nearly constant as documents grow. It calls the result ‘unlimited-horizon’ parsing within a finite context window. Image encoding and total runtime still rise with page count.
How does it score on MPDocBench and MarathonDocBench?
Baidu team tests on MPDocBench, with dozens of pages per document, and MarathonDocBench, with hundreds. MPDocBench-Parse is a public benchmark of 433 documents and 3,246 pages. We could not find a public MarathonDocBench release.
Per Baidu’s leaderboard chart, Unlimited OCR 2 scores 84.6 on MPDocBench. Qwen3.8-Max follows at 78.3, then Qwen-3.8-Flash-Next at 77.4. On MarathonDocBench it scores 77.1. Kimi-K3 is next at 33.7, then Gemini-3.8-Flash at 28.6.
The drop between benchmarks is the real story. Qwen3.8-Max falls from 78.3 to 22.9 on hundred-page documents. Unlimited OCR 2 falls 7.5 points. Its predecessor, Unlimited-OCR, scores 0.0 on MarathonDocBench.
Sub-scores (MPDocBench / MarathonDocBench):
- Text: 94.8 / 89.6
- Formula: 89.4 / 87.9
- Reading order: 92.3 / 85.3
- Tables: 87.8 / 74.8
- Heading hierarchy: 60.1 / 48.2
Single-metric margins on MPDocBench are thin. Qwen3.8-Max scores 94.3 on text and 89.2 on formulas. All figures are self-reported and appear only in a chart image.
Comparison with other open OCR models
Parameter counts and licenses from each model’s Hugging Face page. Benchmark scores from Baidu’s chart.
How do you run Unlimited OCR 2?
The model card lists Transformers for quick tests and SGLang for serving. In Transformers, infer handles 1 page with dynamic tiling, called gundam mode. infer_multi parses every page of a document in 1 request, so the model keeps cross-page context. Loading requires trust_remote_code=True, which runs Python code from the repo.
SGLang serving uses a customized wheel from Baidu, not upstream SGLang. Baidu’s example schedules 10 pages per window and reuses the previous window’s KV tail. Smaller windows cut peak memory but weaken cross-page merging. Baidu warns a default bias setting can trigger a 462 MiB allocation and run out of memory.
Key Takeaways
- Unlimited OCR 2 is a 3.34B, MIT-licensed OCR model from Baidu.
- SR-SWA caps attention at a fixed 512-token reference set.
- It scores 77.1 on MarathonDocBench, against 33.7 for the next model.
- Heading hierarchy is the weak spot, at 48.2 on long documents.
- All benchmark numbers are Baidu’s own.
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Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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