Chandra
An OCR model for complex documents — handwriting, tables, math equations, and messy forms.
Benchmarks
Overall scores on the olmocr bench:
Hosted API
A hosted API with additional accuracy improvements is available at datalab.to. Try the free playground without installing.
Community
Join Discord to discuss development and get help.
Quick Start
pip install chandra-ocr
# Start vLLM server, then run OCR
chandra_vllm
chandra input.pdf ./output
# Or use HuggingFace locally
chandra input.pdf ./output --method hf
# Interactive web app
chandra_app
Python:
from chandra.model import InferenceManager
from chandra.input import load_pdf_images
manager = InferenceManager(method="hf")
images = load_pdf_images("document.pdf")
results = manager.generate(images)
print(results[0].markdown)
How it Works.
- Two inference modes: Run locally via HuggingFace Transformers, or deploy a vLLM server for production throughput
- Layout-aware output: Every text block, table, and image comes with bounding box coordinates
- Structured formats: Output as Markdown, HTML, or JSON with full layout metadata
- 40+ languages supported
What It Handles
Handwriting — Doctor notes, filled forms, homework. Chandra reads cursive and messy print that trips up traditional OCR.
Tables — Preserves structure including merged cells (colspan/rowspan). Works on financial filings, invoices, and data tables.
Math — Inline and block equations rendered as LaTeX. Handles textbooks, worksheets, and research papers.
Forms — Reconstructs checkboxes, radio buttons, and form fields with their values.
Complex Layouts — Multi-column documents, newspapers, textbooks with figures and captions.
Examples
 Handwriting |
 Tables |
 Math |
 Newspapers |
More examples
| Type | Name | Link |
|---|---|---|
| Tables | 10K Filing | View |
| Forms | Lease Agreement | View |
| Handwriting | Math Homework | View |
| Books | Geography Textbook | View |
| Books | Exercise Problems | View |
| Math | Attention Diagram | View |
| Math | Worksheet | View |
| Newspapers | LA Times | View |
| Other | Transcript | View |
| Other | Flowchart | View |
Installation
pip install chandra-ocr
For HuggingFace inference, we recommend installing flash attention for better performance.
From source:
git clone https://github.com/datalab-to/chandra.git
cd chandra
uv sync
source .venv/bin/activate
Usage
CLI
# Single file with vLLM server
chandra input.pdf ./output --method vllm
# Directory with local model
chandra ./documents ./output --method hf
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:
output/
└── filename/
├── filename.md # Markdown
├── filename.html # HTML with bounding boxes
├── filename_metadata.json
└── images/ # Extracted images
vLLM Server
For production or batch processing:
chandra_vllm
Launches a Docker container with optimized inference. Configure via environment:
VLLM_API_BASE: Server URL (default:http://localhost:8000/v1)VLLM_MODEL_NAME: Model name (default:chandra)VLLM_GPUS: GPU device IDs (default:0)
Configuration
Settings via environment variables or local.env:
MODEL_CHECKPOINT=datalab-to/chandra
MAX_OUTPUT_TOKENS=8192
VLLM_API_BASE=http://localhost:8000/v1
VLLM_GPUS=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.