Chandra

Chandra is a highly accurate 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
• Math equation support (LaTeX)
• Reconstructs forms, including checkboxes
• Precise table reconstruction
• Support for 40+ languages
• Two inference modes: local (HuggingFace) and remote (vLLM server)

Installation

uv sync
source .venv/bin/activate

Usage

Streamlit Web App

Launch the interactive demo:

streamlit run chandra_app.py --server.fileWatcherType none --server.headless true

Inference Modes:

• hf: Loads model locally using HuggingFace Transformers (requires GPU)
• vllm: Connects to a running vLLM server for optimized batch inference

vLLM Server (Optional)

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

python scripts/start_vllm.py

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)
• HF_TOKEN: HuggingFace token for model access

Configuration

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

# Model settings
MODEL_CHECKPOINT=datalab-to/chandra-0.2.8
MAX_OUTPUT_TOKENS=8192

# vLLM settings
VLLM_API_BASE=http://localhost:8000/v1
VLLM_MODEL_NAME=chandra
VLLM_GPUS=0

Output Formats

Chandra provides three output formats:

1. HTML: Structured HTML with layout blocks and bounding boxes
2. Markdown: Clean, readable Markdown conversion
3. Layout Image: Visual representation of detected layout blocks