Top 20 OCR Models 2026: Best Local Document Parsers

Originally published: August 4, 2026 · Last updated: October 20, 2026

Introduction to OCR Models

OCR is no longer a single-line text recognizer bolted onto a PDF pipeline. The strongest 2026 models are vision-language document parsers that return clean Markdown, preserve reading order, reconstruct tables and formulas, and ground every block with a bounding box. That structural output is what makes an OCR pass useful for RAG, search, analytics, and agentic document workflows.

This October 2026 ranking focuses on models you can actually run yourself, from a 270M edge recognizer to 5B-class parsers. Proprietary API-only services and legacy engines have been removed. The biggest change since August is the arrival of long-horizon multi-page parsing, stronger sub-1B models, and open models that output layout grounding alongside text.

What changed in this update: Unlimited-OCR, OvisOCR2, PaddleOCR-VL 1.6, Qianfan-OCR, Infinity-Parser2, LightOnOCR-3, and Indra OCR enter or move up. Mistral OCR, GOT-OCR2.0, and the Qwen2.5-VL models leave the ranking because they are API-only or no longer competitive for a local 2026 shortlist.

Ranking Methodology for OCR Models

Extraction Accuracy (35%): Character and word accuracy on printed, scanned, handwritten, and degraded documents, using published OmniDocBench and olmOCR-Bench results where available.

Structured Output (25%): Markdown fidelity, table and formula reconstruction, reading order, layout labels, and bounding-box grounding.

Multilingual Coverage (15%): Breadth of scripts and quality on low-resource languages, not just the language count on a model card.

Resource Efficiency (10%): Parameter count, GPU or CPU throughput, quantization support, and practical deployment requirements.

License & Ecosystem (10%): Commercial usability, tooling, inference support, and community adoption.

Freshness (5%): Whether the model reflects the 2026 generation of document parsing rather than the previous single-page OCR era.

Benchmark caution: Scores are author-reported unless stated otherwise, and OmniDocBench v1.5, OmniDocBench v1.6, and olmOCR-Bench are not directly interchangeable. A model that wins one benchmark is not automatically the best choice for your documents.

Quick Picks

Need	Best starting point	Why
Best overall local parser	Unlimited-OCR	MIT license, 32K context, multi-page parsing, and the strongest adoption of any 2026 release.
Highest reported accuracy	OvisOCR2	96.58 on OmniDocBench v1.6 with only 853M parameters.
Production multilingual parsing	PaddleOCR-VL 1.6	96.33 on OmniDocBench v1.6 plus mature layout, table, formula, chart, and seal support.
Small model, big tables	GLM-OCR	94.62 on OmniDocBench v1.5, 1.33B parameters, MIT license, and ready GGUF/Ollama tooling.
CPU-only deployment	Indra OCR or PP-OCRv6	Indra is a 1.25M-parameter recognizer; PP-OCRv6 is a mature 34.5M pipeline family.
Commercial-friendly maximum quality	Unlimited-OCR, OvisOCR2, PaddleOCR-VL 1.6	MIT or Apache 2.0 licensing removes most redistribution friction.

Top 20 OCR Models

1. Unlimited-OCR - The New Default for Long Documents

Parameters: 3.34B
License: MIT
Strengths: One-shot long-horizon parsing, 32K context, multi-page PDFs, dense Markdown output
Best For: Book-scale PDFs, RAG corpora, and mixed-layout documents
Hardware: ~6.7GB BF16 weights; 12GB GPU recommended, with community GGUF builds for smaller setups

Why it ranks #1: Baidu's Unlimited-OCR is the clearest successor to the original DeepSeek-OCR formula. It parses many pages in a single pass, keeps a 32K context window, and has the strongest real-world pull of any 2026 release: roughly 1.98M downloads and 4,800 likes by mid-October. The MIT license and active vLLM, ModelScope, and ms-swift support make it the safest default for a new local document pipeline. Local model page ->

2. OvisOCR2 - The Accuracy-per-Parameter Leader

Parameters: 853M
License: Apache 2.0
Strengths: 96.58 on OmniDocBench v1.6, end-to-end layout and content parsing, strong tables and formulas
Best For: High-accuracy extraction on a single consumer GPU
Hardware: ~1.7GB BF16 weights; 6GB GPU is usually sufficient

Why it ranks #2: OvisOCR2 is the efficiency story of the year. At under 1B parameters it reports 96.58 on OmniDocBench v1.6 and was presented as the first end-to-end model to top a leaderboard previously dominated by multi-stage pipelines. If your priority is accuracy per gigabyte, this is the model to beat. Local model page ->

3. PaddleOCR-VL 1.6 - The Production Multilingual Workhorse

Parameters: 0.96B
License: Apache 2.0
Strengths: 96.33 on OmniDocBench v1.6, Real5 robustness, layout, tables, formulas, charts, seals, text spotting
Best For: Production document ingestion across scans, photos, skew, and mixed layouts
Hardware: ~1.9GB BF16 weights; 6GB GPU recommended

Why it ranks #3: PaddleOCR-VL 1.6 is the most complete open document pipeline in the ranking. It combines a compact ERNIE-based vision-language model with battle-tested PaddleOCR tooling, strong multilingual coverage, and explicit support for hard real-world captures. Its 96.33 OmniDocBench v1.6 score is essentially tied with OvisOCR2, but the surrounding ecosystem is more mature. Local model page ->

4. GLM-OCR - The Best Small All-Rounder

Parameters: 1.33B
License: MIT
Strengths: 94.62 on OmniDocBench v1.5, excellent tables and formulas, eight languages, GGUF and Ollama support
Best For: Academic papers, financial documents, forms, and local apps that need a small footprint
Hardware: ~2.7GB BF16 weights; 8GB GPU recommended, with quantized builds below 1GB

Why it ranks #4: GLM-OCR remains the best quality-per-parameter choice for structured documents. Zhipu reports 94.62 on OmniDocBench v1.5, ranking first at release, and the model has the tooling story most local users want: vLLM, SGLang, Transformers, Ollama, and community GGUF files. It is also one of the few leaders with a clean MIT license. Local model page ->

5. Qianfan-OCR - The 192-Language Document Intelligence Model

Parameters: 4.74B
License: Apache 2.0
Strengths: 93.12 on OmniDocBench v1.5, 192 languages, 87.9 mean on five KIE benchmarks, chart and layout reasoning
Best For: Multilingual business documents and key-information extraction
Hardware: ~9.5GB BF16 weights; 16GB GPU recommended

Why it ranks #5: Qianfan-OCR is Baidu's document-intelligence specialist rather than a pure text extractor. It combines direct image-to-Markdown parsing with layout analysis, extraction, and chart understanding across 192 languages. The 4.7B size makes it heavier than the leaders, but it is the strongest open option when the job includes understanding a form, not just transcribing it. Local model page ->

6. Infinity-Parser2-Flash - The Accuracy/Speed Sweet Spot

Parameters: 2.21B
License: Apache 2.0
Strengths: 86.0 on olmOCR-Bench, 91.98 on OmniDocBench v1.6, 3.68x faster than the previous 7B parser
Best For: High-volume parsing where a 4B-class model is too slow
Hardware: ~4.4GB BF16 weights; 8-12GB GPU recommended

Why it ranks #6: Infinity-Parser2-Flash is the efficiency member of Infly's new parser family. It gives up only 1.6 olmOCR points to the 35B Pro model while staying small enough for a single workstation GPU. If you need strong tables, formulas, and reading order without a flagship-class deployment, this is one of the most compelling 2026 releases. Hugging Face ->

7. LightOnOCR-3-4B - Grounded Parsing for Local Workflows

Parameters: 4.54B
License: Apache 2.0
Strengths: Layout grounding with bounding boxes, image descriptions, chart-to-table extraction, compact output format
Best For: Retrieval pipelines that need block coordinates and visual context, not just plain text
Hardware: ~9.1GB BF16 weights; 16GB GPU recommended; about 3.36 pages/s at 1540px on supported hardware

Why it ranks #7: LightOnOCR-3-4B arrived on October 6 and is the newest serious open-weight entrant. Its grounding mode labels every block, returns normalized bounding boxes, describes images, and turns charts into HTML tables while using fewer output tokens than several larger competitors. For document AI that needs provenance, this is a better fit than a pure Markdown model. Hugging Face ->

8. Chandra OCR 2 - The Long-Tail Language Specialist

Parameters: 5.30B
License: OpenRAIL-M (commercial threshold applies)
Strengths: 85.8 on olmOCR-Bench, 77.8 multilingual score, 90+ languages, strong handwriting and layout handling
Best For: Multilingual archives, forms, and documents with handwriting or degraded scans
Hardware: ~10.6GB BF16 weights; 16-24GB GPU recommended

Why it ranks #8: Datalab's Chandra 2 is the most credible open model for long-tail languages. It reports 85.8 on olmOCR-Bench and a 12-point multilingual improvement over Chandra 1 across 90+ languages. The OpenRAIL-M weights are free for research and smaller commercial users, but larger companies need to check Datalab's threshold before shipping. Local model page ->

9. DeepSeek-OCR-2 - Efficient Dense Document Parsing

Parameters: 3.39B
License: Apache 2.0
Strengths: Visual causal flow, dynamic resolution, compact visual tokens, clean document Markdown
Best For: Dense scanned pages and RAG ingestion on a 12GB GPU
Hardware: ~6.8GB BF16 weights; 12GB GPU recommended, with community GGUF builds available

Why it ranks #9: DeepSeek-OCR-2 is still one of the most efficient ways to turn dense pages into structured text. Its dynamic-resolution encoder keeps visual token counts manageable, and third-party comparisons put it around 91 on OmniDocBench v1.5. It is no longer the outright leader, but the Apache license, small footprint, and broad inference support keep it highly relevant. Hugging Face ->

10. Jina OCR v1 - Fast, Multilingual, Non-Commercial

Parameters: 3.37B
License: CC BY-NC 4.0
Strengths: 91.14 on OmniDocBench v1.6, 83.4 on olmOCR-Bench, 2.57 pages/s on an A100
Best For: Research, evaluation, and non-commercial high-throughput parsing
Hardware: ~6.7GB BF16 weights; 12GB GPU recommended

Why it ranks #10: Jina's first OCR model is fast and accurate, with a compact visual-token design and strong benchmark numbers. The catch is the CC BY-NC 4.0 license: it is excellent for research and internal evaluation, but not a drop-in choice for a commercial product. Hugging Face ->

11. PaddleOCR PP-OCRv6 - The Edge and CPU Baseline

Parameters: Up to 34.5M in the family; 19M medium recognizer
License: Apache 2.0
Strengths: 86.2 detection Hmean, 83.2 recognition accuracy, 50 languages, tiny CPU footprint
Best For: High-throughput production OCR, edge devices, screenshots, receipts, and clean printed text
Hardware: CPU-first; runs comfortably on edge hardware and small GPUs

Why it ranks #11: PP-OCRv6 is not a generative document parser, and that is the point. The medium tier reaches 86.2 detection Hmean and 83.2 recognition accuracy with roughly 34.5M parameters, beating much larger general VLMs on classic OCR tasks. It is the right choice when latency, cost, and predictable output matter more than layout reasoning. Hugging Face ->

12. Surya OCR 2 - The Layout and Throughput Specialist

Parameters: 686M
License: OpenRAIL-M weights; Apache 2.0 code
Strengths: 83.3 on olmOCR-Bench, 91-language coverage, 5 pages/s on an RTX 5090, layout and table recognition
Best For: Fast batch parsing and document pipelines that need explicit layout output
Hardware: ~1.4GB BF16 weights; 4-6GB GPU or CPU with llama.cpp

Why it ranks #12: Surya 2 is a single compact VLM for layout, OCR, and table recognition, with unusually good throughput for its size. It is especially useful when you need structured block output and a dependable pipeline around it. The weights use a modified OpenRAIL-M license with a revenue threshold, so check the terms before commercial deployment. Hugging Face ->

13. LightOnOCR-2-1B - The Proven 1B Drop-In

Parameters: 1.01B
License: Apache 2.0
Strengths: Strong tables and forms, 11 languages, small memory footprint, mature tooling
Best For: Existing LightOnOCR deployments and low-VRAM document parsing
Hardware: ~2GB BF16 weights; 4-6GB GPU recommended

Why it ranks #13: LightOnOCR-2-1B is no longer the newest model in its own family, but it remains one of the most practical 1B-class OCR models. It is Apache-licensed, widely used, and easy to serve. Choose LightOnOCR-3 if you want grounding and visual understanding; choose this if you want a stable, inexpensive parser. Local model page ->

14. Indra OCR - CPU-First Parsing at Production Speed

Parameters: 1.25M recognizer; 7.3M across default models
License: OpenRAIL
Strengths: About 44 pages/s on 32 CPU cores, 80+ languages, handwriting, PDF text-layer awareness, ONNX runtime
Best For: CPU-only servers, privacy appliances, and high-volume text extraction
Hardware: CPU-only; no GPU required

Why it ranks #14: Indra OCR, released October 10, is the most interesting edge deployment of the month. It uses tiny ONNX recognizers plus a line classifier, runs entirely on CPU, and claims roughly 15x Tesseract's throughput on the same hardware. It will not match a 5B VLM on complex layout reasoning, but for clean text at scale it is exceptionally practical. Hugging Face ->

15. Nemotron OCR v2 - The Enterprise Pipeline

Parameters: Multi-component detector, recognizer, and relational model
License: NVIDIA Open Model License
Strengths: Production detection and recognition, reading-order relations, 11 languages, natural-scene and document images
Best For: Enterprise ingestion, RAG preprocessing, and NVIDIA NeMo deployments
Hardware: GPU pipeline; component-level deployment can reduce memory

Why it ranks #15: Nemotron OCR v2 is a pipeline rather than a single generative model, which makes it predictable and easy to integrate into an NVIDIA-centric stack. It handles detection, transcription, and layout relations separately, so you can trade accuracy for latency. The NVIDIA Open Model License is commercially usable, but it is not OSI-open in the same sense as Apache 2.0. Hugging Face ->

16. FireRed-OCR - A Compact Qwen3-VL Parser

Parameters: 2.13B
License: Apache 2.0
Strengths: Built on Qwen3-VL-2B, document parsing, compact deployment, multilingual foundation
Best For: Teams already serving Qwen3-VL and wanting an OCR-tuned variant
Hardware: ~4.3GB BF16 weights; 8GB GPU recommended

Why it ranks #16: FireRed-OCR is a sensible fine-tune of Qwen3-VL-2B for document extraction. It does not have the benchmark profile of the leaders, but the Apache license, familiar architecture, and 2B size make it an easy model to evaluate and deploy. Hugging Face ->

17. HunyuanOCR 1.5 - Document Parsing and Text Spotting

Parameters: 1.12B
License: Tencent Hunyuan Community License
Strengths: Document parsing, text spotting, information extraction, layout, charts, formulas, and text-image translation
Best For: Multilingual document workflows that need spotting and translation in one model
Hardware: ~2.2GB BF16 weights; 6-8GB GPU recommended

Why it ranks #17: HunyuanOCR 1.5 is unusually feature-complete for its size. It unifies parsing, spotting, extraction, layout, and translation, with over 890k downloads and strong adoption in Chinese-language workflows. The Tencent community license is the main reason it sits below the Apache/MIT models. Local model page ->

18. Falcon-OCR - The 270M Edge Option

Parameters: 270M
License: Apache 2.0
Strengths: Extremely small, document understanding, fast CPU/edge inference
Best For: Embedded apps, offline tools, and low-power document capture
Hardware: ~0.5GB FP32 weights; runs on CPU or a small GPU

Why it ranks #18: Falcon-OCR proves that useful document OCR does not require billions of parameters. It is not the accuracy leader, but the 270M footprint and Apache license make it a strong option for local-first products where model size is the binding constraint. Hugging Face ->

19. dots.ocr - The Established Layout Parser

Parameters: 3.04B
License: MIT
Strengths: Unified layout detection and content recognition, reading order, tables, formulas, 100-language internal benchmark
Best For: Teams that need explicit layout boxes and a permissive license
Hardware: ~6.1GB BF16 weights; 10-12GB GPU recommended

Why it ranks #19: dots.ocr helped define the single-model layout-parser category and still has one of the cleanest MIT-licensed implementations. It has been overtaken on raw accuracy by newer releases, but its layout output and reading-order handling remain useful for document analytics. Hugging Face ->

20. MinerU 2.5 - The Document Extraction Pipeline

Parameters: 1.16B
License: AGPL-3.0
Strengths: Layout, table, formula, and PDF extraction, mature Python tooling, strong Chinese document support
Best For: Self-hosted document conversion where AGPL terms are acceptable
Hardware: ~2.3GB BF16 weights; 6-8GB GPU recommended

Why it ranks #20: MinerU 2.5 is less of a single OCR model and more of a complete extraction pipeline. It remains a dependable choice for PDF-to-Markdown work, especially for Chinese documents, but the AGPL-3.0 license and older benchmark profile keep it at the bottom of this forward-looking list. Local model page ->

Choosing the Right OCR Model

For RAG and Knowledge Bases

Best default: Unlimited-OCR. It handles long documents, returns clean Markdown, and keeps a permissive license.

Best accuracy-first alternative: OvisOCR2 or PaddleOCR-VL 1.6 when you can afford a second parsing pass or a small model ensemble.

For Tables, Forms, and Financial Documents

Best small model: GLM-OCR. Strong table and formula output with an easy local deployment path.

Best high-volume option: Infinity-Parser2-Flash. It keeps most of the accuracy of a much larger parser at 2.21B parameters.

For Multilingual Archives

Broadest language coverage: Qianfan-OCR, with 192 languages and strong key-information extraction.

Best long-tail open model: Chandra OCR 2 for 90+ languages, handwriting, and degraded scans, subject to its OpenRAIL terms.

For CPU-Only or Edge Deployment

Best classic OCR: PaddleOCR PP-OCRv6 for predictable, high-throughput text recognition.

Best tiny parser: Indra OCR for CPU servers, or Falcon-OCR when the model must fit in a very small memory budget.

For Commercial Products

Safest licenses: Unlimited-OCR (MIT), OvisOCR2 (Apache 2.0), PaddleOCR-VL 1.6 (Apache 2.0), GLM-OCR (MIT), Qianfan-OCR (Apache 2.0), and Infinity-Parser2-Flash (Apache 2.0).

Check before shipping: Jina OCR v1 is CC BY-NC 4.0; Chandra and Surya use OpenRAIL variants with commercial thresholds; HunyuanOCR uses the Tencent community license; MinerU is AGPL-3.0; Nemotron uses the NVIDIA Open Model License.

Best Practices for OCR Deployment

• Pre-process before parsing: Deskew, denoise, correct orientation, and render PDFs at 200-300 DPI. Real5-OmniDocBench exists because real captures are harder than clean scans.
• Preserve structure, not just text: Prefer models that output Markdown plus tables, formulas, and reading order. Plain text extraction destroys the relationships RAG needs.
• Keep page provenance: Store page numbers and bounding boxes when the model supports them. LightOnOCR-3, dots.ocr, and PaddleOCR-VL are useful here.
• Validate on your own documents: A benchmark winner can lose to a smaller model on receipts, handwriting, seals, or a specific language. Build a 50-100 page evaluation set from your actual corpus.
• Measure end-to-end retrieval quality: OCR accuracy is an intermediate metric. The real test is whether the extracted text improves retrieval and answer quality after chunking and embedding.
• Plan for quantization: BF16 is the reference configuration, but GGUF, AWQ, and ONNX builds are what make these models practical on 8-16GB machines. Re-test accuracy after quantization.
• Chain OCR to retrieval: Pair the output with the best embedding models and a precision reranker to complete the local pipeline.

Conclusion

The 2026 OCR market has split into three useful tiers: sub-1B accuracy leaders such as OvisOCR2 and PaddleOCR-VL 1.6, practical all-rounders such as Unlimited-OCR and GLM-OCR, and CPU-first engines such as PP-OCRv6 and Indra OCR. The right choice depends on whether your bottleneck is accuracy, language coverage, page volume, or licensing.

For most local RAG projects, start with Unlimited-OCR and keep PaddleOCR-VL 1.6 as the accuracy and multilingual cross-check. If you need the smallest strong parser, test OvisOCR2. If you need tables and formulas on modest hardware, test GLM-OCR. Every model in this list can run without sending documents to a third-party API, which is the entire point of a local document stack.

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📖 Educational Content Index

🏆 Model Rankings

Use Case	Description	Link
Coding Assistant	Best models for programming	View Guide
Research Assistant	Top models for academic research	View Guide
Analysis & BI	Models for data analysis	View Guide
Embedding	Top models for RAG and semantic search	View Guide
Reranker	Precision re-ranking models	View Guide
OCR	Document text extraction models	View Guide ← You are here
Multilingual	Models with superior language support	View Guide

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🔄 Last Updated: October 20, 2026 | 📧 Feedback