surya-ocr-2

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).

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Model Information

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

Examples

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
  • label - canonicalized layout label (e.g. Text, SectionHeader, Table, Equation, Picture, Form, PageHeader, ...). See surya/layout/label.py:LAYOUT_PRED_RELABEL for the full canonical-name set.
  • raw_label - original label emitted by the model, before canonicalization
  • reading_order - 0-indexed position in layout output
  • html - block content as HTML (math wrapped in ..., tables as ..., etc.). "" if the block was skipped
  • polygon - 4-corner polygon in [[x0,y0],[x1,y0],[x1,y1],[x0,y1]] order
  • bbox - axis-aligned [x0, y0, x1, y1] derived from the polygon
  • confidence - mean per-token probability across the block's decode (0-1)
  • skipped - true if the block was a visual label (e.g. Picture) and not OCR'd
  • error - true if the block OCR call failed
• 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, rais