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datalab-to/chandra-ocr-2

sourceHugging Faceopenrailupdated 3mo agoView on Hugging Face

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Model Card

Chandra OCR 2

Chandra 2 is a state of the art OCR model from Datalab that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information.

Try Chandra in the free playground, or use the hosted API for higher accuracy and speed.

What's New in Chandra 2

—85.8% olmocr bench score (sota), 77.8% multilingual bench score (12% improvement over Chandra 1)
—Significant improvements to math, tables, complex layouts
—Improved layout, especially on wider documents
—Significantly better image captioning
—90+ language support with major accuracy gains

Features

—Convert documents to markdown, HTML, or JSON with detailed layout information
—Excellent handwriting support
—Reconstructs forms accurately, including checkboxes
—Strong performance with tables, math, and complex layouts
—Extracts images and diagrams, with captions and structured data
—Support for 90+ languages

Quickstart

shell

pip install chandra-ocr

# With vLLM (recommended, easy install)
chandra_vllm
chandra input.pdf ./output

# With HuggingFace (requires torch)
pip install chandra-ocr[hf]
chandra input.pdf ./output --method hf

Usage

With vLLM (recommended)

python

from chandra.model import InferenceManager
from chandra.model.schema import BatchInputItem
from PIL import Image

# Start vLLM server first with: chandra_vllm
manager = InferenceManager(method="vllm")
batch = [
    BatchInputItem(
        image=Image.open("document.png"),
        prompt_type="ocr_layout"
    )
]
result = manager.generate(batch)[0]
print(result.markdown)

With HuggingFace Transformers

python

from transformers import AutoModelForImageTextToText, AutoProcessor
from chandra.model.hf import generate_hf
from chandra.model.schema import BatchInputItem
from chandra.output import parse_markdown
from PIL import Image
import torch

model = AutoModelForImageTextToText.from_pretrained(
    "datalab-to/chandra-ocr-2",
    dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()
model.processor = AutoProcessor.from_pretrained("datalab-to/chandra-ocr-2")
model.processor.tokenizer.padding_side = "left"

batch = [
    BatchInputItem(
        image=Image.open("document.png"),
        prompt_type="ocr_layout"
    )
]

result = generate_hf(batch, model)[0]
markdown = parse_markdown(result.raw)
print(markdown)

Benchmarks

olmOCR Benchmark

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

Examples

Type	Name	Link
Tables	Statistical Distribution	View
Tables	Financial Table	View
Forms	Registration Form	View
Forms	Lease Form	View
Math	CS229 Textbook	View
Math	Handwritten Math	View
Math	Chinese Math	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

Multilingual Benchmark (43 Languages)

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

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%

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.