We have a hosted API for Chandra here, which also includes other accuracy improvements and document workflows.
There is a free playground here if you want to try it out without installing.

Quickstart

The easiest way to start is with the CLI tools:

pip install chandra-ocr

# With VLLM
chandra_vllm
chandra input.pdf ./output

# With HuggingFace
chandra input.pdf ./output --method hf

# Interactive streamlit app
chandra_app

Benchmarks

Model	ArXiv	Old Scans Math	Tables	Old Scans	Headers and Footers	Multi column	Long tiny text	Base	Overall
Datalab Chandra v0.1.0	81.4	80.3	89.4	50.0	88.3	81.0	91.6	99.9	82.7 ± 0.9
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
Mistral OCR API	77.2	67.5	60.6	29.3	93.6	71.3	77.1	99.4	72.0 ± 1.1
Deepseek OCR	75.2	67.9	79.1	32.9	96.1	66.3	78.5	97.7	74.2 ± 1.0
GPT-4o (Anchored)	53.5	74.5	70.0	40.7	93.8	69.3	60.6	96.8	69.9 ± 1.1
Gemini Flash 2 (Anchored)	54.5	56.1	72.1	34.2	64.7	61.5	71.5	95.6	63.8 ± 1.2
Qwen 3 VL	70.2	75.1	45.6	37.5	89.1	62.1	43.0	94.3	64.6 ± 1.1
olmOCR v0.3.0	78.6	79.9	72.9	43.9	95.1	77.3	81.2	98.9	78.5 ± 1.1

Examples

Type	Name	Link
Tables	Water Damage Form	View
Tables	10K Filing	View
Forms	Handwritten Form	View
Forms	Lease Agreement	View
Handwriting	Doctor Note	View
Handwriting	Math Homework	View
Books	Geography Textbook	View
Books	Exercise Problems	View
Math	Attention Diagram	View
Math	Worksheet	View
Math	EGA Page	View
Newspapers	New York Times	View
Newspapers	LA Times	View
Other	Transcript	View
Other	Flowchart	View

Installation

Package

pip install chandra-ocr

From Source

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:

# 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: 1)

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.)
images/ - Extracted images from the document

Streamlit Web App

Launch the interactive demo for single-page processing:

chandra_app

vLLM Server (Optional)

For production deployments or batch processing, use the vLLM server:

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 model.

Configuration

Settings can be configured via environment variables or a local.env file:

# Model settings
MODEL_CHECKPOINT=datalab-to/chandra
MAX_OUTPUT_TOKENS=8192

# 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.

Credits

Thank you to the following open source projects: