The open-source OCR landscape has changed dramatically in 2026. We’ve gone from Tesseract being the only real option to having half a dozen capable models, some of which rival cloud APIs in quality. If you want OCR that runs on your hardware, doesn’t cost per page, and keeps your documents private, here’s what’s available right now.

I’ve tested all of these. Some are excellent. Some are overhyped. Let me save you the experimentation time.

The Comparison Table

Model	Params	License	Languages	Multi-page	Tables	Equations	Min Hardware	Best For
Baidu Unlimited-OCR	3B	MIT	40+	Yes (single pass)	HTML	LaTeX	8 GB RAM	Multi-page PDFs, structured output
GOT-OCR 2.0	580M	Apache 2.0	20+	No	Limited	LaTeX	4 GB VRAM	Academic papers, sheet music
Florence-2	770M	MIT	100+	No	No	No	4 GB VRAM	General vision tasks, captioning
Nougat	350M	CC-BY-NC	English-focused	No	Yes	LaTeX	4 GB VRAM	Academic PDFs, arXiv papers
Tesseract 5	N/A	Apache 2.0	100+	No	No	No	512 MB RAM	Simple text, minimal hardware
DeepSeek-OCR 2	1.3B	MIT	30+	No	Limited	LaTeX	6 GB VRAM	Chinese/English documents

1. Baidu Unlimited-OCR

The current best all-rounder for open-source OCR.

Released June 22, 2026. Built on the DeepSeek-OCR architecture with a SAM+CLIP vision encoder and DeepSeek-V2 MoE decoder. The standout feature is multi-page processing: up to 40 pages in a single forward pass with constant KV cache memory usage.

Strengths:

• Multi-page PDF in one shot (32K context window)
• Structured output: tables as HTML, equations as LaTeX
• Layout-aware with bounding boxes
• Multiple deployment options (vLLM, Ollama, MLX, GGUF)
• MIT license for commercial use

Weaknesses:

• Newer model, less battle-tested in production
• 40+ languages is good but not comprehensive
• Requires GPU for reasonable speed (CPU works but is slow)
• llama.cpp support requires unmerged PR branch

Hardware: 8 GB VRAM (full precision) or 6 GB RAM (GGUF Q4 on CPU)

For the complete setup guide, see How to Run Baidu Unlimited-OCR Locally. For how it compares to paid services, read our Mistral OCR 4 vs DeepSeek Vision vs Baidu Unlimited-OCR comparison.

2. GOT-OCR 2.0

Best for academic content and specialized document types.

GOT-OCR (General OCR Theory) takes an interesting approach: it treats OCR as a visual generation task. Rather than traditional text detection + recognition pipelines, it generates text directly from visual features. Version 2.0 adds sheet music recognition and improved formula handling.

Strengths:

• Excellent equation and formula extraction
• Sheet music OCR (unique capability)
• Relatively small (580M parameters)
• Fast inference on modest hardware
• Good at preserving document structure

Weaknesses:

• Limited language support (primarily English, Chinese, a few others)
• No multi-page processing
• Tables are hit-or-miss
• Less active community than larger projects

Hardware: 4 GB VRAM GPU minimum. Runs well on RTX 3060 and above.

Best for: Researchers processing papers with heavy math, musicians digitizing sheet music, or anyone working primarily with English/Chinese academic content.

3. Florence-2

Best as a general vision foundation model that happens to do OCR.

Florence-2 from Microsoft is a vision-language model designed for many tasks: captioning, object detection, OCR, visual grounding, and more. It’s not a dedicated OCR model, but its text recognition capabilities are solid, especially given its small size.

Strengths:

• Very versatile (OCR is just one of many capabilities)
• Good multilingual support (100+ languages)
• Small and fast (770M params)
• MIT license
• Active Microsoft support and updates

Weaknesses:

• Not optimized for OCR specifically
• No structured table or equation extraction
• Layout awareness is basic
• Output is plain text without formatting preservation

Hardware: 4 GB VRAM minimum. Can run on many consumer GPUs.

Best for: Projects that need OCR as one of several vision capabilities. If you’re already using Florence-2 for image understanding and need “good enough” text extraction without adding another model.

4. Nougat

Best for converting academic PDFs to structured Markdown.

Nougat (Neural Optical Understanding for Academic documents using GROBID as Training) was built specifically for converting academic papers into machine-readable Markdown. It handles complex layouts, multi-column text, equations, and tables that are common in scientific publications.

Strengths:

• Excellent on academic paper layouts
• Equations rendered as LaTeX
• Tables preserved structurally
• Small model (350M params)
• Fast inference

Weaknesses:

• CC-BY-NC license (no commercial use)
• English-focused (struggles with other languages)
• Trained specifically on arXiv-style papers
• Not great for general business documents
• No active updates since initial release

Hardware: 4 GB VRAM. Very lightweight.

Best for: Academic researchers who need to convert published papers into editable, searchable Markdown. Not suitable for commercial applications due to licensing.

5. Tesseract 5

The old reliable. Still useful in specific scenarios.

Tesseract has been around for decades (originally HP, then Google). Version 5 uses an LSTM-based recognition engine. It’s not a neural model in the modern sense, it doesn’t understand document structure, tables, or equations. But it does basic text extraction reliably across many languages.

Strengths:

• Runs on anything (512 MB RAM, no GPU needed)
• 100+ languages with community-trained models
• Apache 2.0 license
• Decades of production use and debugging
• Extremely well-documented
• Simple CLI tool, easy to script

Weaknesses:

• No document structure understanding
• No table or equation handling
• Requires clean input (sensitive to skew, noise, low resolution)
• Raw text output only
• Accuracy significantly below modern neural models on complex docs

Hardware: Essentially any computer. A Raspberry Pi can run Tesseract.

Best for: Legacy systems, extremely resource-constrained environments, simple single-language text extraction where document structure doesn’t matter. Also useful as a fast pre-filter before sending complex documents to a heavier model.

6. DeepSeek-OCR 2

The predecessor architecture that inspired Unlimited-OCR.

DeepSeek-OCR 2 is the model that Baidu’s Unlimited-OCR builds upon. It established the SAM+CLIP encoder approach for document understanding. It’s smaller (1.3B params) and lacks multi-page capability, but it’s well-tested and reliable.

Strengths:

• Proven architecture
• Good Chinese/English bilingual performance
• MIT license
• Solid equation and basic table handling
• Well-documented with community resources

Weaknesses:

• Single-page only (no multi-page PDF support)
• Smaller language coverage than Unlimited-OCR
• Being superseded by Unlimited-OCR
• Fewer deployment options

Hardware: 6 GB VRAM for comfortable operation.

Best for: Teams already using DeepSeek models who need single-page OCR without upgrading to the larger Unlimited-OCR. Also useful if you need a smaller model that still handles CJK well.

For more on DeepSeek’s vision capabilities (including OCR), see our DeepSeek Vision complete guide and the OCR-specific tutorial.

How to Choose

Start here:

“I need the best open-source OCR with no restrictions” Go with Baidu Unlimited-OCR. MIT license, best quality, multi-page support, structured output.

“I’m processing academic papers” Try Nougat first (if non-commercial) or GOT-OCR 2.0 (if commercial). Both handle equations and academic layouts well.

“I have very limited hardware” Tesseract if you need no GPU at all. Florence-2 or GOT-OCR if you have at least 4 GB VRAM.

“I need 100+ language support” Florence-2 or Tesseract for breadth. Unlimited-OCR for quality with 40+ languages.

“I process multi-page documents” Only Baidu Unlimited-OCR handles this in a single pass. Everything else requires page-by-page processing with manual reassembly.

“I need this for production commercial use” MIT or Apache 2.0 licensed options: Unlimited-OCR, Florence-2, GOT-OCR, Tesseract, or DeepSeek-OCR 2. Avoid Nougat (CC-BY-NC).

The Cloud Alternative

Sometimes open-source isn’t the right call. If you need:

• The absolute best accuracy (especially on hard documents)
• Enterprise SLA and support
• Zero infrastructure management
• 170 language support

Then paid services like Mistral OCR 4 ($4/1K pages) or Google Document AI ($5/1K pages) are worth considering. See our multimodal AI APIs price comparison for the full cloud pricing landscape.

The sweet spot for many teams: use open-source models for development, testing, and standard documents, then route the hard cases to a cloud API. This hybrid approach gives you the best of both worlds.

What’s Coming Next

The open-source OCR space is accelerating. Things to watch:

• Larger context windows enabling even longer documents in single passes
• Better handwriting recognition (still a gap for all open models)
• More languages with fewer parameters
• Native video/scanned-book OCR (page turning detection)
• Better integration with RAG pipelines (chunking-aware extraction)

2026 has already been the best year for open-source OCR. The gap between free and paid is shrinking fast.

FAQ

Which open-source OCR model has the best accuracy?

Baidu Unlimited-OCR currently leads among open models for general document processing. GOT-OCR 2.0 may edge it out on academic papers with heavy equations. Neither matches Mistral OCR 4’s 72% blind test win rate, but for most documents the difference is negligible.

Can any of these replace Google Document AI or Mistral OCR 4?

For standard business documents (invoices, contracts, forms): yes, Unlimited-OCR produces usable output for most cases. For edge cases (low quality scans, rare languages, complex nested tables), cloud services still have an advantage. Many teams use a hybrid approach.

Which model runs fastest on consumer hardware?

Tesseract is the lightest (no GPU needed). Among neural models, GOT-OCR 2.0 and Nougat are smallest and fastest. Florence-2 is also quick. Unlimited-OCR is larger but offers the best quality-to-resource ratio.

Is there an open-source model that handles handwriting?

None of these handle handwriting well. It’s the biggest remaining gap in open-source OCR. For handwritten documents, cloud services (Google, Mistral) still significantly outperform open alternatives. Some fine-tuned versions of Florence-2 show promise but aren’t production-ready.

Can I fine-tune these models for my specific documents?

Yes, all the MIT/Apache-licensed models can be fine-tuned. Unlimited-OCR and Florence-2 have the most community resources for fine-tuning. Tesseract supports training custom language/font models. Fine-tuning on your specific document types can dramatically improve accuracy.

Do any of these support real-time OCR (video/camera)?

Not directly. These are designed for static document images. For real-time camera OCR (like scanning receipts with a phone), you’d typically use a lighter detection model to find text regions, then feed cropped regions to one of these models. Tesseract is fast enough for near-real-time on simple text.