Datalab
State of the Art models for Document Intelligence
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.
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News
- 3/2026 - Chandra 2 is here with significant improvements to math, tables, layout, and multilingual OCR
- 10/2025 - Chandra 1 launched
Features
- Tops external olmocr benchmark and significant improvement in internal multilingual benchmarks
- Convert documents to markdown, html, or json with detailed layout information
- Support for 90+ languages (benchmark below)
- Excellent handwriting support
- Reconstructs forms accurately, including checkboxes
- Strong performance with tables, math, and complex layouts
- Extracts images and diagrams, and adds captions and structured data
- Two inference modes: local (HuggingFace) and remote (vLLM server)
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:
--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: 28 for vllm, 1 for hf)
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.)- Extracted images are saved directly in the output directory
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-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 OCR 2 是一款业界领先的 OCR 模型，可以把图片和 PDF 转换为结构化的 HTML、Markdown 或 JSON，并保留原始版式。它能处理复杂表格、表单、手写内容和数学公式，支持 90 多种语言。