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NeuralMetrics/
Nanonets-OCR-s

Name: Nanonets-OCR-s
Availability: InStock

Qwen/Qwen2.5-VL-3B-Instruct

finetune

text-generation-inference

endpoints_compatible

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Neural Metrics · OCR that outputs structured Markdown, not soup.

Purpose-built for document-to-Markdown conversion with semantic tagging - it marks up tables, signatures, watermarks, checkboxes and page numbers rather than flattening everything into one text stream.

We use it for: high-fidelity document conversion - detecting checkboxes and signatures - preserving semantic structure into downstream parsing.

Attribution

This is an unmodified fork of nanonets/Nanonets-OCR-s, created by the Qwen team. All weights, files and behaviour are identical to upstream — we rehost it so our experiments stay reproducible and version-pinned. The original license and all credit remain with the Qwen team. If you want the canonical model, please use the original.

Original model card from nanonets/Nanonets-OCR-s (click to expand)

Nanonets-OCR-s by Nanonets is a powerful, state-of-the-art image-to-markdown OCR model that goes far beyond traditional text extraction. It transforms documents into structured markdown with intelligent content recognition and semantic tagging, making it ideal for downstream processing by Large Language Models (LLMs).

Nanonets-OCR-s is packed with features designed to handle complex documents with ease:

LaTeX Equation Recognition: Automatically converts mathematical equations and formulas into properly formatted LaTeX syntax. It distinguishes between inline ( $...$ ) and display ($$...$$) equations.
Intelligent Image Description: Describes images within documents using structured <img> tags, making them digestible for LLM processing. It can describe various image types, including logos, charts, graphs and so on, detailing their content, style, and context.
Signature Detection & Isolation: Identifies and isolates signatures from other text, outputting them within a <signature> tag. This is crucial for processing legal and business documents.
Watermark Extraction: Detects and extracts watermark text from documents, placing it within a <watermark> tag.
Smart Checkbox Handling: Converts form checkboxes and radio buttons into standardized Unicode symbols (☐, ☑, ☒) for consistent and reliable processing.
Complex Table Extraction: Accurately extracts complex tables from documents and converts them into both markdown and HTML table formats.

📢 Read the full announcement | 🤗 Hugging Face Space Demo

Usage

Using transformers

python

from PIL import Image
from transformers import AutoTokenizer, AutoProcessor, AutoModelForImageTextToText

model_path = "nanonets/Nanonets-OCR-s"

model = AutoModelForImageTextToText.from_pretrained(
    model_path, 
    torch_dtype="auto", 
    device_map="auto", 
    attn_implementation="flash_attention_2"
)
model.eval()

tokenizer = AutoTokenizer.from_pretrained(model_path)
processor = AutoProcessor.from_pretrained(model_path)


def ocr_page_with_nanonets_s(image_path, model, processor, max_new_tokens=4096):
    prompt = """Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation. If there is an image in the document and image caption is not present, add a small description of the image inside the <img></img> tag; otherwise, add the image caption inside <img></img>. Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>. Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number> or <page_number>9/22</page_number>. Prefer using ☐ and ☑ for check boxes."""
    image = Image.open(image_path)
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": [
            {"type": "image", "image": f"file://{image_path}"},
            {"type": "text", "text": prompt},
        ]},
    ]
    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt")
    inputs = inputs.to(model.device)
    
    output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
    generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
    
    output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
    return output_text[0]

image_path = "/path/to/your/document.jpg"
result = ocr_page_with_nanonets_s(image_path, model, processor, max_new_tokens=15000)
print(result)

Using vLLM

Start the vLLM server.

vllm serve nanonets/Nanonets-OCR-s

Predict with the model

python

from openai import OpenAI
import base64

client = OpenAI(api_key="123", base_url="http://localhost:8000/v1")

model = "nanonets/Nanonets-OCR-s"

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

def ocr_page_with_nanonets_s(img_base64):
    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/png;base64,{img_base64}"},
                    },
                    {
                        "type": "text",
                        "text": "Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation. If there is an image in the document and image caption is not present, add a small description of the image inside the <img></img> tag; otherwise, add the image caption inside <img></img>. Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>. Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number> or <page_number>9/22</page_number>. Prefer using ☐ and ☑ for check boxes.",
                    },
                ],
            }
        ],
        temperature=0.0,
        max_tokens=15000
    )
    return response.choices[0].message.content

test_img_path = "/path/to/your/document.jpg"
img_base64 = encode_image(test_img_path)
print(ocr_page_with_nanonets_s(img_base64))

Using docext

python

pip install docext
python -m docext.app.app --model_name hosted_vllm/nanonets/Nanonets-OCR-s

Checkout GitHub for more details.

BibTex

@misc{Nanonets-OCR-S,
  title={Nanonets-OCR-S: A model for transforming documents into structured markdown with intelligent content recognition and semantic tagging},
  author={Souvik Mandal and Ashish Talewar and Paras Ahuja and Prathamesh Juvatkar},
  year={2025},
}

NeuralMetrics/Nanonets-OCR-s

Neural Metrics · OCR that outputs structured Markdown, not soup.

Attribution

Usage

Using transformers

Using vLLM

Using docext

BibTex

NeuralMetrics/
Nanonets-OCR-s