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Surya is an open-source document OCR toolkit for extracting text and document structure across many languages. It can recognize page text, detect text lines, identify layout regions such as tables and images, and— in Surya 2—detect reading order and recognize tables. You can run it yourself or use Datalab’s hosted platform. The right choice depends on your language and document mix, deployment needs, and licensing requirements.

What Surya OCR does

Surya is designed for document pages rather than only clean, isolated text. The project describes OCR support for more than 90 languages, line-level text detection in any language, and layout analysis for regions including tables, images, and headers. Example document types include scanned pages, forms, textbooks, newspapers, scientific papers, and presentations.

Datalab describes Surya 2 as a 650-million-parameter model combining four tasks: full-page OCR, layout analysis, reading-order detection, and table recognition. Line detection is described separately in the project’s training notes, so it should not be confused with those four Surya 2 tasks.

How accurate and fast is it?

Datalab reports an 83.3% result on olmOCR-bench. Its internal evaluation across 91 languages reports an 87.2% pass rate overall, with 38 languages scoring at least 90%. These are publisher-reported figures, not independent tests, and an aggregate language score does not establish performance for every language, script, scan quality, handwriting style, or document layout.

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Datalab also reports throughput of 5.35 pages per second on one RTX 5090 at 128 concurrent requests. Treat that as a result under the stated GPU and concurrency conditions, not a general speed guarantee for a different machine or workload.

The published figures do not provide an apples-to-apples comparison with Tesseract or cloud OCR services. To compare systems for your use case, run the same representative documents through each option and assess text accuracy, structure, latency, and operating cost.

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How to install and run Surya

Repository setup

  1. Check the environment: the repository documents Python 3.9 or later and PyTorch.
  2. Install the package: run pip install surya-ocr.
  3. Run an initial job: model weights download automatically on first use. The repository documents device auto-detection and optional use of a Streamlit GUI.

The documented repository setup pins Transformers 4.36.2; that repository snapshot says Transformers 4.37 and newer were not supported. This is a compatibility note for that documented setup, not a guarantee about later package releases. Check the current repository instructions before changing dependencies.

Surya 2 serving backends

Datalab describes vLLM for NVIDIA GPU deployment and llama.cpp for CPU or Apple Silicon. NVIDIA deployments may require Docker and the NVIDIA Container Toolkit; CPU and Apple Silicon users need a llama.cpp llama-server binary. These Surya 2 serving requirements are distinct from the repository’s general package-install instructions, so confirm the requirements for the particular release and backend you plan to use.

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Can Surya run without a GPU?

Local CPU use is an option: Datalab describes llama.cpp for CPU deployments, as well as for Apple Silicon. The repository also documents automatic device detection. A GPU is therefore not an absolute requirement for every local setup, but no CPU speed figure is provided. Test your expected page volume and latency on the hardware you intend to operate.

Can Surya extract tables from PDFs?

Surya 2 includes table recognition, and Surya’s layout analysis identifies table regions. That makes it a candidate for documents where table structure matters, but it does not guarantee that every PDF table will be reconstructed correctly. PDF source quality, complex or merged cells, and surrounding layout can affect the result. Verify the extracted rows, columns, and values against the original pages before relying on them downstream.

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Choose self-hosted Surya or Datalab’s platform

Option What it offers What you take on
Self-hosted Local processing on CPU, Apple Silicon, or NVIDIA GPU; can suit privacy-sensitive or offline workflows. You manage dependencies, suitable hardware, model serving, scaling, and maintenance.
Datalab hosted platform Hosted API and SDK, versioned pipelines, managed cloud, EU data residency, VPC deployment, and on-premises or air-gapped options. Evaluate the service’s fit for your data, workflow, operating requirements, and budget.

Compare both approaches using the same four criteria: accuracy on your language and layout mix, latency and throughput, data residency and compliance, and total operating cost. For self-hosting, include hardware availability and the work of running and maintaining the model.

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What Datalab’s published plans cost

Plan Published price or allowance
Free Allowance described as $20 per month with a work email or $10 per month with a personal email.
Team $400 per month.
Enterprise Custom pricing.

These are the plans and allowances stated on Datalab’s pricing page in the available information; confirm current terms directly with Datalab before budgeting.

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Licensing: code and model weights differ

Surya’s code is licensed under Apache 2.0, while its model weights use a modified OpenRAIL-M license. Datalab’s announcement describes free use for research, personal projects, and startups under $5 million. Its pricing page separately says licensing depends on funding or revenue thresholds and directs larger commercial users toward licensing or hosted or on-premises arrangements. Because code and weights have different terms, do not assume that an Apache-licensed codebase makes every commercial use of the weights unrestricted. Check the current weight license and obtain written guidance from Datalab for a business outside the stated thresholds.

How to evaluate Surya for production

Build a test set that reflects the documents the system will actually process, rather than relying only on aggregate benchmark scores. Include the languages, scripts, scan conditions, handwriting (if relevant), table designs, and document domains in your workflow.

Quick Recap

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  • Measure character or word accuracy for extracted text.
  • Check whether reading order matches the page, especially in multi-column layouts.
  • Compare detected layout regions and table structures with the source documents.
  • Evaluate downstream extraction quality: whether the recognized content is usable in the next step of your workflow.
  • Measure latency and throughput on the intended hardware or hosted plan at realistic concurrency.
  • Review dependency compatibility, backend requirements, data residency, and license terms for the release you intend to deploy.

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