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<p align="center">
<img src="assets/datalab-logo.png" alt="Datalab Logo" width="150"/>
</p>
<h1 align="center">Datalab</h1>
<p align="center">
<strong>State of the Art models for Document Intelligence</strong>
</p>
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<hr/>
# Chandra OCR 2
Chandra OCR 2 is a state of the art OCR model that converts images and PDFs into structured HTML/Markdown/JSON while preserving layout information.
## Try Chandra on Datalab
Our managed platform runs an improved Chandra with higher accuracy than the open weights, zero data retention by default, SOC 2 Type 2, and custom BAAs.
If you have high volume workloads, we offer a batch processing service that has processed 200M+ pages per week — we manage the infrastructure so your workloads finish on time.
Get started with **$5 in free credits** — [sign up](https://www.datalab.to/?utm_source=gh-chandra) — takes under 30 seconds — or try Chandra in our [public playground](https://www.datalab.to/playground?utm_source=gh-chandra).
Commercial self-hosting requires a license — see [Commercial usage](#commercial-usage). For on-prem licensing, [contact us](https://www.datalab.to/contact?utm_source=gh-chandra-onprem).
## News
- 3/2026 - Chandra 2 is here with significant improvements to math, tables, layout, and multilingual OCR
- 10/2025 - Chandra 1 launched
## Features
- Tops external olmocr benchmark and significant improvement in internal multilingual benchmarks
- Convert documents to markdown, html, or json with detailed layout information
- Support for 90+ languages ([benchmark below](#multilingual-benchmark-table))
- Excellent handwriting support
- Reconstructs forms accurately, including checkboxes
- Strong performance with tables, math, and complex layouts
- Extracts images and diagrams, and adds captions and structured data
- Two inference modes: local (HuggingFace) and remote (vLLM server)
<img src="assets/examples/math/handwritten_math.png" width="600px"/>
## Quickstart
The easiest way to start is with the CLI tools:
```shell
pip install chandra-ocr
# With vLLM (recommended, lightweight install)
chandra_vllm
chandra input.pdf ./output
# With HuggingFace (requires torch)
pip install chandra-ocr[hf]
chandra input.pdf ./output --method hf
# Interactive streamlit app
pip install chandra-ocr[app]
chandra_app
```
## Benchmarks
Multilingual performance was a focus for us with Chandra 2. There isn't a good public multilingual OCR benchmark, so we made our own. This tests tables, math, ordering, layout, and text accuracy.
<img src="assets/benchmarks/multilingual.png" width="600px"/>
See full scores [below](#multilingual-benchmark-table). We also have a [full 90-language benchmark](FULL_BENCHMARKS.md).
We also benchmarked Chandra 2 with the widely accepted olmocr benchmark:
<img src="assets/benchmarks/bench.png" width="600px"/>
See full scores [below](#benchmark-table).
## Examples
| Type | Name | Link |
|------|--------------------------|-------------------------------------------------------------------------------------------------------------|
| 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) |
| 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) |
| 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) |
## Installation
### Package
```bash
# Base install (for vLLM backend)
pip install chandra-ocr
# With HuggingFace backend (includes torch, transformers)
pip install chandra-ocr[hf]
# With all extras
pip install chandra-ocr[all]
```
If you're using the HuggingFace method, we also recommend installing [flash attention](https://github.com/Dao-AILab/flash-attention) for better performance.
### From Source
```bash
git clone https://github.com/datalab-to/chandra.git
cd chandra
uv sync
source .venv/bin/activate
```
## Usage
### CLI
Process single files or entire directories:
```bash
# Single file, with vllm server (see below for how to launch vllm)
chandra input.pdf ./output --method vllm
# Process all files in a directory with local model
chandra ./documents ./output --method hf
```
**CLI Options:**
- `--method [hf|vllm]`: Inference method (default: vllm)
- `--page-range TEXT`: Page range for PDFs (e.g., "1-5,7,9-12")
- `--max-output-tokens INTEGER`: Max tokens per page
- `--max-workers INTEGER`: Parallel workers for vLLM
- `--include-images/--no-images`: Extract and save images (default: include)
- `--include-headers-footers/--no-headers-footers`: Include page headers/footers (default: exclude)
- `--batch-size INTEGER`: Pages per batch (default: 28 for vllm, 1 for hf)
**Output Structure:**
Each processed file creates a subdirectory with:
- `<filename>.md` - Markdown output
- `<filename>.html` - HTML output
- `<filename>_metadata.json` - Metadata (page info, token count, etc.)
- Extracted images are saved directly in the output directory
### Streamlit Web App
Launch the interactive demo for single-page processing:
```bash
chandra_app
```
### vLLM Server (Optional)
For production deployments or batch processing, use the vLLM server:
```bash
chandra_vllm
```
This launches a Docker container with optimized inference settings. Configure via environment variables:
- `VLLM_API_BASE`: Server URL (default: `http://localhost:8000/v1`)
- `VLLM_MODEL_NAME`: Model name for the server (default: `chandra`)
- `VLLM_GPUS`: GPU device IDs (default: `0`)
You can also start your own vllm server with the `datalab-to/chandra-ocr-2` model.
### Configuration
Settings can be configured via environment variables or a `local.env` file:
```bash
# Model settings
MODEL_CHECKPOINT=datalab-to/chandra-ocr-2
MAX_OUTPUT_TOKENS=12384
# vLLM settings
VLLM_API_BASE=http://localhost:8000/v1
VLLM_MODEL_NAME=chandra
VLLM_GPUS=0
```
# Commercial usage
This code is Apache 2.0, and our 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). To remove the OpenRAIL license requirements, or for broader commercial licensing, visit our pricing page [here](https://www.datalab.to/pricing?utm_source=gh-chandra).
# Benchmark table
| **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 |
# Multilingual benchmark table
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-table).
| 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 table
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](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% |
# Credits
Thank you to the following open source projects:
- [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)
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