Baidu拥有发布无限制的OCR 2，一种为非常长的文档所建的开源 OCR模型。 3B模式在一次请求中剖析了数百页的书籍，归档和报告。 其核心变化是SR-SWA，是一种很少的注意计划，它使逐步解码成本几乎平平。 麻省理工学院许可下的重量船，其代码为GitHub.
TL; DR 数据
- 尺寸：3.34B参数。 未披露活动参数。 最大位置长度为131,072个令牌。
- 运行在：1 CUDA GPU在BF16。 Baidu在NVIDIA A800-80GB上进行测试。 BF16重量总计约 6.7 GB.
- 性能：在两个多页的基准测试 Baidu测试中排名第一位，比照其他9个模型(自报).
- 最佳：在马拉松道克本奇(上百页)上总共77.1个。 下个型号为Kimi-K3，得分33.7.
- 最糟糕的是：在马拉松道克本克(马拉松道克本克)的分级结构(TEDS)上48.2分，这是其最低分数。
什么是无限制 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为结构化文本。 产出包括文本、表格、公式、数字、标题和阅读顺序。 它于2026年6月接替了无限制-OCR，发布，以进一步推进DeepSeek-OCR.
配置保持了DeepSeek-OCR风格布局。 一台SAM VIT-B外加CLIP-L编码器能提供12层混合型专家解码器。 每条标致路线通向64位专家中的6位，外加2位共享专家。 DeepSeek-OCR纸将这款解码器描述为DeepSeek3B-MoE-A570M. Baidu没有声明2版的有效计数。 在多页模式下，每页花费273个图像符号。
SR -SWA是怎么工作的？
完全关注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较长的KV缓存。 每页有273个图像信使，100页在任何输出前会增加27300个输入信使。
SR-SWA代表斯帕斯参考滑动窗口关注。 它将文字-视觉注意力压缩为一套固定的工作参考。 Baidu的型号卡给出了512个令牌作为示例大小。 配置对一对128个托肯本地窗口，在32个托肯区块之上有前16个选择。 这是16块32个符牌符合512块的预算
这样，随着文件的增长，每一步的间隔和记忆几乎保持不变。 它称结果为"无限范围"，图像编码和总运行时间随着页数而上升。
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；马拉松多克 Bench则有上百页。 MPDoc Bench-Parse是一个由433个文件和3,246页组成的公共基准测试。 我们找不到一个公开的马拉松道克奔驰。
Per Baidu的领跑板图，无限OCR 2在MPDoc Bench上得分84.6分。 Qwen3.8-Max跟随78.3，再到Qwen-3.8-Flash-Next77.4. 在马拉松道克奔驰上，它的得分是77.1分，Kimi-K3次以33.7分，再以28.6分Gemini-3.8-Flash.
基准测试之间的投放是真实的故事。 Qwen3.8-Max从78.3跌至22.9分百页文档。 无限制OCR 2跌出7.5分。 其前身"无限-OCR"在马拉松道克本奇上得分0.0.
分分数(MPDoc Bench /马拉松道克 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在文本上得分为94.3分，在公式上得分为89.2分。 所有数字都是自报的，只出现在图表图中。
与其他开放式 OCR 模型的比较
从每个型号的Hugging Face页计算参数和许可证。 基准测试从Baidu的图中得分。
如何运行无限制 OCR 2?
模式卡列出"变形金刚"用于快速测试，而SGLang则用于服务。 在"变形金刚"(Transformers)中，推算手柄为一页有活性平板，称为gundam模式。 推断1个请求中文档的每页都含有多解析值，因此模型保持了跨页上下文。 装入需要信任 remote code=True 从repo运行 Python 代码。
SGLang服务使用Baidu的定制轮子，而非上游SGLang. 兹克 1Z的示例列表每个窗口有10页，并重用上个窗口的KV尾巴。 更小的windows剪切了峰值内存但削弱了跨页合并。 Baidu 警告一个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个参考集吸引注意力。
- 马拉松道克本奇的得分是77.1，而下一个型号的得分是33.7.
- 头级是弱点，长文件48.2。
- 所有基准测试号码都是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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