README.md

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

<p align="center">
  <img src="datalab-logo.png" alt="Datalab Logo" width="150"/>
</p>

# Chandra OCR 2

Chandra 2 is a state of the art OCR model from [Datalab](https://www.datalab.to) that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information.

Try Chandra in the [free playground](https://www.datalab.to/playground), or use the [hosted API](https://www.datalab.to/) for higher accuracy and speed.

## What's New in Chandra 2

- 85.9% olmocr bench score (sota), 77.8% multilingual bench score (12% improvement over Chandra 1)
- Significant improvements to math, tables, complex layouts
- Improved layout, especially on wider documents
- Significantly better image captioning
- 90+ language support with major accuracy gains

## Features

- Convert documents to markdown, HTML, or JSON with detailed layout information
- Excellent handwriting support
- Reconstructs forms accurately, including checkboxes
- Strong performance with tables, math, and complex layouts
- Extracts images and diagrams, with captions and structured data
- Support for 90+ languages

<img src="handwritten_form.png" width="600px"/>

## Quickstart

```shell
pip install chandra-ocr

# With vLLM (recommended, easy install)
chandra_vllm
chandra input.pdf ./output

# With HuggingFace (requires torch)
pip install chandra-ocr[hf]
chandra input.pdf ./output --method hf
```

## Usage

### With vLLM (recommended)

```python
from chandra.model import InferenceManager
from chandra.model.schema import BatchInputItem
from PIL import Image

# Start vLLM server first with: chandra_vllm
manager = InferenceManager(method="vllm")
batch = [
    BatchInputItem(
        image=Image.open("document.png"),
        prompt_type="ocr_layout"
    )
]
result = manager.generate(batch)[0]
print(result.markdown)
```

### With HuggingFace Transformers

```python
from transformers import AutoModelForImageTextToText, AutoProcessor
from chandra.model.hf import generate_hf
from chandra.model.schema import BatchInputItem
from chandra.output import parse_markdown
from PIL import Image
import torch

model = AutoModelForImageTextToText.from_pretrained(
    "datalab-to/chandra-ocr-2",
    dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()
model.processor = AutoProcessor.from_pretrained("datalab-to/chandra-ocr-2")
model.processor.tokenizer.padding_side = "left"

batch = [
    BatchInputItem(
        image=Image.open("document.png"),
        prompt_type="ocr_layout"
    )
]

result = generate_hf(batch, model)[0]
markdown = parse_markdown(result.raw)
print(markdown)
```

## Benchmarks

### olmOCR Benchmark

<img src="bench.png" width="600px"/>

| **Model** |  ArXiv   | Old Scans Math |  Tables  | Old Scans | Headers and Footers | Multi column | Long tiny text | Base |    Overall     | Source |
|:----------|:--------:|:--------------:|:--------:|:---------:|:-------------------:|:------------:|:--------------:|:----:|:--------------:|:------:|
| Datalab API               | **90.4** | **90.2** | **90.7** | **54.6** |        91.6         |     83.7     |    **92.3**    | **99.9** | **86.7 ± 0.8** | Own benchmarks |
| Chandra 2                 |   90.2   |   89.3   |   89.9   |   49.8   |        92.5         |     83.5     |      92.1      | 99.6 |   85.9 ± 0.8   | Own benchmarks |
| dots.ocr 1.5              |   85.9   |   85.5   | **90.7** |   48.2   |        94.0         |   **85.3**   |      81.6      | 99.7 |   83.9         | dots.ocr repo |
| Chandra 1                 |   82.2   |   80.3   |   88.0   |   50.4   |        90.8         |     81.2     |    **92.3**    | **99.9** |   83.1 ± 0.9   | Own benchmarks |
| olmOCR 2                  |   83.0   |   82.3   |   84.9   |   47.7   |      **96.1**       |     83.7     |      81.9      | 99.6 |   82.4         | olmocr repo |
| dots.ocr                  |   82.1   |   64.2   |   88.3   |   40.9   |        94.1         |     82.4     |      81.2      | 99.5 |   79.1 ± 1.0   | dots.ocr repo |
| olmOCR v0.3.0             |   78.6   |   79.9   |   72.9   |   43.9   |        95.1         |     77.3     |      81.2      | 98.9 |   78.5 ± 1.1   | olmocr repo |
| Datalab Marker v1.10.0    |   83.8   |   69.7   |   74.8   |   32.3   |        86.6         |     79.4     |      85.7      | 99.6 |   76.5 ± 1.0   | Own benchmarks |
| Deepseek OCR              |   75.2   |   72.3   |   79.7   |   33.3   |      **96.1**       |     66.7     |      80.1      | 99.7 |   75.4 ± 1.0   | Own benchmarks |
| Mistral OCR API           |   77.2   |   67.5   |   60.6   |   29.3   |        93.6         |     71.3     |      77.1      | 99.4 |   72.0 ± 1.1   | olmocr repo |
| GPT-4o (Anchored)         |   53.5   |   74.5   |   70.0   |   40.7   |        93.8         |     69.3     |      60.6      | 96.8 |   69.9 ± 1.1   | olmocr repo |
| Qwen 3 VL 8B              |   70.2   |   75.1   |   45.6   |   37.5   |        89.1         |     62.1     |      43.0      | 94.3 |   64.6 ± 1.1   | Own benchmarks |
| Gemini Flash 2 (Anchored) |   54.5   |   56.1   |   72.1   |   34.2   |        64.7         |     61.5     |      71.5      | 95.6 |   63.8 ± 1.2   | olmocr repo |

## Examples

| Type | Name | Link |
|------|------|------|
| Tables | Statistical Distribution | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/complex_tables.png) |
| Tables | Financial Table | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/financial_table.png) |
| Forms | Registration Form | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/handwritten_form.png) |
| Forms | Lease Form | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/lease_filled.png) |
| Math | CS229 Textbook | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/cs229.png) |
| Math | Handwritten Math | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/handwritten_math.png) |
| Math | Chinese Math | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/chinese_math.png) |
| Handwriting | Cursive Writing | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/cursive_writing.png) |
| Handwriting | Handwritten Notes | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/handwritten_notes.png) |
| Languages | Arabic | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/arabic.png) |
| Languages | Japanese | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/japanese.png) |
| Languages | Hindi | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/hindi.png) |
| Languages | Russian | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/russian.png) |
| Other | Charts | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/charts.png) |
| Other | Chemistry | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/chemistry.png) |


### Multilingual Benchmark (43 Languages)

The table below covers the 43 most common languages, benchmarked across multiple models. For a comprehensive evaluation across 90 languages (Chandra 2 vs Gemini 2.5 Flash only), see the [full 90-language benchmark](#full-90-language-benchmark).

<img src="multilingual.png" width="600px"/>

| Language | Datalab API | Chandra 2 | Chandra 1 | Gemini 2.5 Flash | GPT-5 Mini |
|---|:---:|:---:|:---:|:---:|:---:|
| ar | 67.6% | 68.4% | 34.0% | 84.4% | 55.6% |
| bn | 85.1% | 72.8% | 45.6% | 55.3% | 23.3% |
| ca | 88.7% | 85.1% | 84.2% | 88.0% | 78.5% |
| cs | 88.2% | 85.3% | 84.7% | 79.1% | 78.8% |
| da | 90.1% | 91.1% | 88.4% | 86.0% | 87.7% |
| de | 93.8% | 94.8% | 83.0% | 88.3% | 93.8% |
| el | 89.9% | 85.6% | 85.5% | 83.5% | 82.4% |
| es | 91.8% | 89.3% | 88.7% | 86.8% | 97.1% |
| fa | 82.2% | 75.1% | 69.6% | 61.8% | 56.4% |
| fi | 85.7% | 83.4% | 78.4% | 86.0% | 84.7% |
| fr | 93.3% | 93.7% | 89.6% | 86.1% | 91.1% |
| gu | 73.8% | 70.8% | 44.6% | 47.6% | 11.5% |
| he | 76.4% | 70.4% | 38.9% | 50.9% | 22.3% |
| hi | 80.5% | 78.4% | 70.2% | 82.7% | 41.0% |
| hr | 93.4% | 90.1% | 85.9% | 88.2% | 81.3% |
| hu | 88.1% | 82.1% | 82.5% | 84.5% | 84.8% |
| id | 91.3% | 91.6% | 86.7% | 88.3% | 89.7% |
| it | 94.4% | 94.1% | 89.1% | 85.7% | 91.6% |
| ja | 87.3% | 86.9% | 85.4% | 80.0% | 76.1% |
| jv | 87.5% | 73.2% | 85.1% | 80.4% | 69.6% |
| kn | 70.0% | 63.2% | 20.6% | 24.5% | 10.1% |
| ko | 89.1% | 81.5% | 82.3% | 84.8% | 78.4% |
| la | 78.0% | 73.8% | 55.9% | 70.5% | 54.6% |
| ml | 72.4% | 64.3% | 18.1% | 23.8% | 11.9% |
| mr | 80.8% | 75.0% | 57.0% | 69.7% | 20.9% |
| nl | 90.0% | 88.6% | 85.3% | 87.5% | 83.8% |
| no | 89.2% | 90.3% | 85.5% | 87.8% | 87.4% |
| pl | 93.8% | 91.5% | 83.9% | 89.7% | 90.4% |
| pt | 97.0% | 95.2% | 84.3% | 89.4% | 90.8% |
| ro | 86.2% | 84.5% | 82.1% | 76.1% | 77.3% |
| ru | 88.8% | 85.5% | 88.7% | 82.8% | 72.2% |
| sa | 57.5% | 51.1% | 33.6% | 44.6% | 12.5% |
| sr | 95.3% | 90.3% | 82.3% | 89.7% | 83.0% |
| sv | 91.9% | 92.8% | 82.1% | 91.1% | 92.1% |
| ta | 82.9% | 77.7% | 50.8% | 53.9% | 8.1% |
| te | 69.4% | 58.6% | 19.5% | 33.3% | 9.9% |
| th | 71.6% | 62.6% | 47.0% | 66.7% | 53.8% |
| tr | 88.9% | 84.1% | 68.1% | 84.1% | 78.2% |
| uk | 93.1% | 91.0% | 88.5% | 87.9% | 81.9% |
| ur | 54.1% | 43.2% | 28.1% | 57.6% | 16.9% |
| vi | 85.0% | 80.4% | 81.6% | 89.5% | 83.6% |
| zh | 87.8% | 88.7% | 88.3% | 70.0% | 70.4% |
| **Average** | **80.4%** | **77.8%** | **69.4%** | **67.6%** | **60.5%** |

### Full 90-Language Benchmark

We also have a more comprehensive evaluation covering 90 languages, comparing Chandra 2 against Gemini 2.5 Flash. The average scores are lower than the 43-language table above because this includes many lower-resource languages. Chandra 2 averages **72.7%** vs Gemini 2.5 Flash at **60.8%**.

See the [full 90-language results](https://github.com/datalab-to/chandra/blob/master/FULL_BENCHMARKS.md).

## Throughput

Benchmarked with vLLM on a single NVIDIA H100 80GB GPU using a diverse mix of documents (math, tables, scans, multi-column layouts) from the olmOCR benchmark set.  This set is significantly slower than real-world usage - we estimate 2 pages/s in real-world usage.

| Configuration | Pages/sec | Avg Latency | P95 Latency | Failure Rate |
|---|:---:|:---:|:---:|:---:|
| vLLM, 96 concurrent sequences | 1.44 | 60s | 156s | 0% |

## Commercial Usage

Code is Apache 2.0. Model weights use a modified OpenRAIL-M license: free for research, personal use, and startups under $2M funding/revenue. Cannot be used competitively with our API. For broader commercial licensing, see [pricing](https://www.datalab.to/pricing?utm_source=gh-chandra).

## Credits

- [Huggingface Transformers](https://github.com/huggingface/transformers)
- [vLLM](https://github.com/vllm-project/vllm)
- [olmocr](https://github.com/allenai/olmocr)
- [Qwen 3.5](https://github.com/QwenLM/Qwen3)