Instructions to use nanonets/Nanonets-OCR-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nanonets/Nanonets-OCR-s with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="nanonets/Nanonets-OCR-s")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("nanonets/Nanonets-OCR-s")
model = AutoModelForImageTextToText.from_pretrained("nanonets/Nanonets-OCR-s")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nanonets/Nanonets-OCR-s with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nanonets/Nanonets-OCR-s"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nanonets/Nanonets-OCR-s",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/nanonets/Nanonets-OCR-s

SGLang

How to use nanonets/Nanonets-OCR-s with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nanonets/Nanonets-OCR-s" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nanonets/Nanonets-OCR-s",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nanonets/Nanonets-OCR-s" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nanonets/Nanonets-OCR-s",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use nanonets/Nanonets-OCR-s with Docker Model Runner:
```
docker model run hf.co/nanonets/Nanonets-OCR-s
```

Issues with Multi-Column Text Recognition and the Need for hOCR Support

#20

by techana - opened Jul 1, 2025

Discussion

techana

Jul 1, 2025

This is a great project. I tested it with a variety of Arabic texts, and it recognizes the content quite well. However, one issue I noticed is that it struggles with multi-column layouts, especially when a page contains three or more columns. In a test page from an old Arabic magazine with three columns, the model processed the two rightmost columns but ignored the left one. I tried adjusting different settings, but it never managed to process the entire page.

Another issue I encountered with columned articles is the messy output. The model sometimes treats adjacent columns as a single text block, combining lines from different columns into one line in the resulting text. This brings me to the feature request mentioned in the title: support for hOCR output. Having hOCR data would greatly simplify the creation of searchable PDFs, which could then be used in training datasets.

Souvik3333

Nanonets org Jul 1, 2025

Thanks for sharing. We will look into the hOCR data format. Feel free to share if you find any other edge cases. We would love to fix as many as possible in our next release. If possible, do share one example here; it will be easier for us to collect similar data.

techana

Jul 1, 2025

Sure, here are two example images for the same page—one in color and the other in grayscale. I deskewed the images by 1 degree in one run to improve the results, but that didn’t help, and the model still couldn’t recognize the whole pages.

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