v0.20.0

# Surya 2 (v0.20.0)

Surya 2 is a ground-up rework: a single **650M-param model** now handles OCR, layout, and table recognition, served by `vllm` (NVIDIA GPU) or `llama.cpp` (CPU / Apple Silicon). Text detection and OCR-error detection remain separate lightweight torch models.

> ⚠️ **This is a major release with breaking API and output-schema changes.** See *Upgrading from v1* below.

## Highlights

- **State of the art for its size** - 83.3% on [olmOCR-bench](https://huggingface.co/datasets/allenai/olmOCR-bench), best in class under 3B params.
- **Multilingual** - 87.2% average across a 91-language internal benchmark.
- **Fast** - 5 pages/s throughput on RTX 5090.

## Breaking changes — upgrading from v1

```python
# 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])
```

- `SuryaInferenceManager` replaces `FoundationPredictor`, and is shared across `LayoutPredictor`, `RecognitionPredictor`, and `TableRecPredictor`.
- **Output schemas changed:** `text_lines` → `blocks` (each with `html`); layout dropped `top_k` and added `count`; table-rec cells dropped `is_header` / `colspan` / `rowspan`.
- **New runtime requirement:** layout / OCR / table-rec need an inference backend - Docker + the NVIDIA Container Toolkit (GPU), or `brew install llama.cpp` (CPU / Apple Silicon). Detection still runs on torch alone.

## Installation

```shell
pip install surya-ocr
```

Then make a backend available (see *Breaking changes* above). Full usage, output schemas, and tuning notes are in the [README](https://github.com/datalab-to/surya#readme).

## Notes

- Try it without installing anything on the [Datalab playground](https://www.datalab.to/playground?utm_source=gh-surya).

**Full Changelog**: https://github.com/datalab-to/surya/compare/v0.17.1...v0.20.0