7.1 KiB
Chandra
Chandra is an OCR model that converts images and PDFs into structured HTML/Markdown/JSON while preserving layout information.
Features
- Convert documents to markdown, html, or json with detailed layout information
- Good handwriting support
- Reconstructs forms accurately, including checkboxes
- Good support for tables, math, and complex layouts
- Extracts images and diagrams, with captions and structured data
- Support for 40+ languages
- Two inference modes: local (HuggingFace) and remote (vLLM server)
Hosted API
- 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: