chandra README

The project's README from github.com/datalab-to/chandra. As one markdown file: README.md.

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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 — takes under 30 seconds — or try Chandra in our public playground.

Commercial self-hosting requires a license — see Commercial usage. For on-prem licensing, contact us.

News

Features

Quickstart

The easiest way to start is with the CLI tools:

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.

See full scores below. We also have a full 90-language benchmark.

We also benchmarked Chandra 2 with the widely accepted olmocr benchmark:

See full scores below.

Examples

Type Name Link
Math CS229 Textbook View
Math Handwritten Math View
Math Chinese Math View
Tables Statistical Distribution View
Tables Financial Table View
Forms Registration Form View
Forms Lease Form View
Handwriting Cursive Writing View
Handwriting Handwritten Notes View
Languages Arabic View
Languages Japanese View
Languages Hindi View
Languages Russian View
Other Charts View
Other Chemistry View

Installation

Package

# 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 for better performance.

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:

Output Structure:

Each processed file creates a subdirectory with:

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:

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:

# 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.

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 86.9 89.1 92.1 51.1 91.4 82.1 93.7 99.9 85.8 ± 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.

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.

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:

chandra (datalab-to/chandra)

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Chandra
Что это такое?Chandra OCR 2 — передовая модель распознавания текста (OCR), которая превращает изображения и PDF в структурированный HTML, Markdown или JSON, сохраняя исходную разметку. Она справляется со сложными таблицами, формами, рукописным текстом и формулами и поддерживает более 90 языков.
Нужно ли запускать это самостоятельно или это только облачный API?Можно запускать модель у себя локально через HuggingFace или подключаясь к серверу vLLM, а также воспользоваться управляемой облачной версией Datalab с публичной песочницей и пакетной обработкой.
Как с его помощью обрабатывать документы?Установите пакет, затем используйте CLI для обработки отдельных файлов или целых папок, выбрав способ инференса 'hf' или 'vllm'. Результат сохраняется для каждого файла в виде .md, .html и metadata.json. Также доступно веб-приложение на Streamlit для интерактивной обработки одной страницы.
Можно ли бесплатно использовать Chandra в своём продукте?Код распространяется по лицензии Apache 2.0, но веса модели используют изменённую лицензию OpenRAIL-M. Она бесплатна только для исследований, личного использования и стартапов с финансированием или выручкой менее 2 млн долларов, и нельзя использовать модель в конкуренции с собственным API Datalab. Более широкое коммерческое самостоятельное развёртывание требует платной лицензии.
Сохраняет ли облачная версия мои данные?Управляемая платформа Datalab по умолчанию не хранит данные (нулевое хранение данных), соответствует стандарту SOC 2 Type 2 и поддерживает индивидуальные соглашения BAA для чувствительных задач.
Чем Chandra отличается от других моделей OCR?По собственным тестам Datalab, Chandra 2 набирает 85,8% в целом, опережая dots.ocr 1.5, olmOCR 2 и Deepseek OCR. По точности на многих языках она лидирует со средним показателем 77,8% против 67,6% у Gemini 2.5 Flash.
Какие языки он поддерживает?Chandra 2 поддерживает более 90 языков. Опубликованные тесты охватывают 43 распространённых языка, а отдельная оценка на 90 языках проведена в сравнении с Gemini 2.5 Flash.
В какие форматы можно экспортировать результат?Документы конвертируются в Markdown, HTML или JSON с сохранением таблиц, формул и форм с флажками. Также извлекаются изображения и диаграммы с подписями и структурированными данными.
Насколько это быстро?При работе vLLM с 96 одновременными последовательностями на GPU H100 в тестовом наборе обрабатывается около 1,44 страницы в секунду. Datalab оценивает реальную скорость ближе к 2 страницам в секунду.
What are similar projects?surya, lift, Unlimited-OCR, olmocr, MinerU, PDF-Extract-Kit, docling, marker
Which topics is it in?table-extraction, vision-language-model, vllm, ocr, document-processing, multilingual, json, pdf, nvidia, huggingface, computer-vision, cloud
Creator
datalab-to
Size
22.9 MB
Stars
12.5K
Updated
CommitJun 26Releasev0.2.0Mar 18
Age
1y
Commits · 90 Days