surya ocr 2

---
library_name: transformers
license: openrail
license_link: LICENSE
tags:
- ocr
- pdf
- markdown
- layout
---

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<strong>State of the Art models for Document Intelligence</strong>
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Surya

Surya is a 650M param OCR model with these features:

• Accuracy - scores 83.3% on olmOCR-bench (top under 3B params)

• Speed - throughput of 5 pages/s on an RTX 5090

• Multilingual - scores 87.2% on an internal benchmark set of 91 languages (more here)

• Layout analysis (table, image, header, etc.) with reading order

• Table recognition (rows + columns)

It works on a range of documents (see usage and benchmarks).

Try Datalab's Managed Platform

Our managed platform runs both Surya, and variants of our highest accuracy model, Chandra.

Get started with $5 in free credits — sign up (takes under 30 seconds) or try our free public playground.

Model Information

<img src="assets/olmocr_size_chart.png" width="700"/>

| Detection | OCR |
|:----------------------------------------------------------------:|:-----------------------------------------------------------------------:|
| <img src="assets/excerpt.png" width="140"/> | <img src="assets/excerpt_text.png" width="140"/> |

| Layout | Table Recognition |
|:------------------------------------------------------------------:|:-------------------------------------------------------------:|
| <img src="assets/excerpt_layout.png" width="140"/> | <img src="assets/scanned_tablerec.png" width="140"/> |

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

Examples

| Name | Detection | OCR | Layout | Order | Table Rec |
|-------------------|:-----------------------------------:|------------------------------------------:|---------------------------------------------:|------------------------------------------------:|------------------------------------------------:|
| Newspaper | Image | Image | Image | Image | |
| Textbook | Image | Image | Image | Image | |
| Tax Form | Image | Image | Image | Image | Image |
| Handwritten Notes | Image | Image | Image | Image | Image |
| Corporate Doc | Image | Image | Image | Image | Image |

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

Usage

Surya 2 runs layout, OCR, and table recognition through a single VLM served
by vllm (GPU) or llama.cpp (CPU / Apple Silicon). The inference manager
will spawn one for you on first use; you can also point it at an existing
server via SURYA_INFERENCE_URL=http://host:port/v1.

• Inspect the settings in surya/settings.py. You can override any setting via env var (e.g. SURYA_INFERENCE_BACKEND=vllm).

• Text detection and OCR errors are separate models.

Interactive App

I've included a streamlit app that lets you interactively try Surya on images or PDF files. Run it with:

pip install streamlit pdftext
surya_gui

OCR (text recognition)

This command will write out a json file with the detected text and bboxes:

surya_ocr DATA_PATH

• DATA_PATH can be an image, pdf, or folder of images/pdfs

• --images will save images of the pages and detected blocks (optional)

• --output_dir specifies the directory to save results to instead of the default

• --page_range specifies the page range to process in the PDF, specified as a single number, a comma separated list, a range, or comma separated ranges - example: 0,5-10,20.

The results.json file contains a dict keyed by input filename (no extension). Each value is a list of page dicts. Each page dict contains:

• blocks - per-block OCR results in reading order

• image_bbox - [0, 0, width, height] for the page image

Performance tips

Throughput is governed by the inference backend, not a RECOGNITION_BATCH_SIZE env var. With vllm, raise --max-num-seqs / --max-num-batched-tokens (or SURYA_INFERENCE_PARALLEL on the client side) to keep more pages in flight. With llama.cpp, set SURYA_INFERENCE_PARALLEL to match --parallel on llama-server.

From python

from PIL import Image
from surya.inference import SuryaInferenceManager
from surya.recognition import RecognitionPredictor
manager = SuryaInferenceManager()
recognition_predictor = RecognitionPredictor(manager)
Default: full-page OCR. One VLM call per page; returns layout + content as
HTML <div data-bbox=... data-label=...> blocks.
predictions = recognition_predictor([Image.open(IMAGE_PATH)])
Block mode: pre-run layout, then per-block OCR. Auto-selected when
layout_results is passed.
from surya.layout import LayoutPredictor
layout = LayoutPredictor(manager)
layouts = layout([Image.open(IMAGE_PATH)])
predictions = recognition_predictor([Image.open(IMAGE_PATH)], layouts)

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