surya-ocr-2.5

Surya is a 689M param OCR model with these features:

• Accuracy - scores 85.9 on OmniParseBench, the highest of the open models under 1B parameters we have scored, and 83.7% on olmOCR-bench
• Speed - throughput of 4.5 pages/s on one H100 at 192 DPI, 1.7x Surya 2 with MTP (see Throughput)
• Multilingual - scores 74.9% on an internal benchmark set of 91 languages (more here)
• Layout analysis (table, image, header, etc.) with reading order
• Tables as HTML, with merged cells as colspan / rowspan (row and column geometry needs a Surya 2 checkpoint; see Limitations)

We also ship smaller models for line-level text detection and ocr error detection. It works on a range of documents (see usage and benchmarks).

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Model Information

Surya is named for the Hindu sun god, who has universal vision.

Examples

Each row links to four annotated views of the same page: text-line detection, OCR, layout and reading order (Surya 2.5).

The example pages are public pages from OmniParseBench. The newspaper (The Alpha, 1881), reference book and math paper are from olmOCR-bench (Ai2, ODC-BY 1.0). The government form is a US federal form (public domain). The handwritten letter is from the Library of Congress, Manuscript Division, Carrie Chapman Catt Papers (public domain). The corporate page is from ParseBench (Apache 2.0). The Japanese form is made from a form of the National Tax Agency, Japan, under the Public Data License 1.0: 「国税庁の様式」（国税庁）（https://www.nta.go.jp/）を加工して作成. The OCR column renders Surya 2.5's HTML output; layout and reading order are drawn from its blocks, and the detection image is from the text-line detector.

Commercial usage

The Surya code is licensed under Apache 2.0. The model weights use a modified AI Pubs Open Rail-M license (free for research, personal use, and startups under $5M funding/revenue). For broader commercial licensing of the model weights, visit our pricing page here.

Installation

Install with:

pip install surya-ocr

Inference backend prerequisites

Surya auto-spawns the server on first use, and you need vllm (NVIDIA GPU) or llama.cpp (CPU / Apple Silicon):

• NVIDIA GPU: Docker plus the NVIDIA Container Toolkit.
• CPU / Apple Silicon: the llama-server binary from llama.cpp:

  brew install llama.cpp     # macOS
  # or grab a release from https://github.com/ggml-org/llama.cpp/releases
  

Upgrading from Surya v1

If you have v1 code, you can migrate to this:

# v2
from surya.inference import SuryaInferenceManager
from surya.recognition import RecognitionPredictor

manager = SuryaInferenceManager()              # auto-spawns vllm or llama-server
rec = RecognitionPredictor(manager)
predictions = rec([image])

What's different:

• SuryaInferenceManager replaces FoundationPredictor. Same manager instance is shared across `La