diff --git a/olmocr/bench/README.md b/olmocr/bench/README.md
index d831641..d4b2f14 100644
--- a/olmocr/bench/README.md
+++ b/olmocr/bench/README.md
@@ -84,6 +84,18 @@ to run it against your own OCR tools. Your tool just needs to support Markdown o
| 99.4 |
72.0 ± 1.1 |
+
+ | Nanonets OCR |
+ 67.0 |
+ 68.6 |
+ 77.7 |
+ 39.5 |
+ 40.7 |
+ 69.9 |
+ 53.4 |
+ 99.3 |
+ 64.5 ± 1.1 |
+
| GPT-4o (No Anchor) |
51.5 |
diff --git a/olmocr/bench/convert.py b/olmocr/bench/convert.py
index a5fd7be..e6d81d0 100644
--- a/olmocr/bench/convert.py
+++ b/olmocr/bench/convert.py
@@ -223,6 +223,7 @@ if __name__ == "__main__":
available_methods = {
"olmocr_pipeline": ("olmocr.bench.runners.run_olmocr_pipeline", "run_olmocr_pipeline"),
"gotocr": ("olmocr.bench.runners.run_gotocr", "run_gotocr"),
+ "nanonetsocr": ("olmocr.bench.runners.run_nanonetsocr", "run_nanonetsocr"),
"marker": ("olmocr.bench.runners.run_marker", "run_marker"),
"mineru": ("olmocr.bench.runners.run_mineru", "run_mineru"),
"chatgpt": ("olmocr.bench.runners.run_chatgpt", "run_chatgpt"),
diff --git a/olmocr/bench/runners/run_nanonetsocr.py b/olmocr/bench/runners/run_nanonetsocr.py
new file mode 100644
index 0000000..22d8579
--- /dev/null
+++ b/olmocr/bench/runners/run_nanonetsocr.py
@@ -0,0 +1,89 @@
+import base64
+import os
+import re
+import tempfile
+
+import torch
+from PIL import Image
+from transformers import AutoModelForImageTextToText, AutoProcessor, AutoTokenizer
+
+from olmocr.data.renderpdf import render_pdf_to_base64png
+
+_model = None
+_tokenizer = None
+_processor = None
+_device = None
+
+
+def load_model(model_path: str = "nanonets/Nanonets-OCR-s"):
+ global _model, _tokenizer, _processor, _device
+
+ if _model is None:
+ _device = "cuda" if torch.cuda.is_available() else "cpu"
+ _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)
+
+ return _model, _tokenizer, _processor
+
+
+async def run_nanonetsocr(pdf_path: str, page_num: int = 1, model_path: str = "nanonets/Nanonets-OCR-s", max_new_tokens: int = 4096, **kwargs) -> str:
+ """
+ Convert page of a PDF file to markdown using NANONETS-OCR.
+
+ This function renders the first page of the PDF to an image, runs OCR on that image,
+ and returns the OCR result as a markdown-formatted string.
+
+ Args:
+ pdf_path (str): The local path to the PDF file.
+
+ Returns:
+ str: The OCR result in markdown format.
+ """
+
+ model, tokenizer, processor = load_model(model_path)
+
+ image_base64 = render_pdf_to_base64png(pdf_path, page_num=page_num, target_longest_image_dim=1024)
+
+ with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
+ image_data = base64.b64decode(image_base64)
+ temp_file.write(image_data)
+ temp_image_path = temp_file.name
+
+ try:
+ image = Image.open(temp_image_path)
+ 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 ![]()
tag; otherwise, add the image caption inside ![]()
. Watermarks should be wrapped in brackets. Ex: OFFICIAL COPY. Page numbers should be wrapped in brackets. Ex: 14 or 9/22. Prefer using ☐ and ☑ for check boxes."""
+ messages = [
+ {"role": "system", "content": "You are a helpful assistant."},
+ {
+ "role": "user",
+ "content": [
+ {"type": "image", "image": f"file://{temp_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", use_fast=True)
+ inputs = inputs.to(model.device)
+ with torch.no_grad():
+ 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)
+ cleaned_text = re.sub(r"\d+", "", output_text[0])
+
+ return cleaned_text
+
+ finally:
+ try:
+ os.unlink(temp_image_path)
+ except Exception as e:
+ print(f"Warning: Failed to remove temporary file {temp_image_path}: {e}")