ggosline/fdg-vol18-nanonets2

Document OCR using Nanonets-OCR2-3B

This dataset contains markdown-formatted OCR results from images in ggosline/fdg-vol18-images using Nanonets-OCR2-3B.

Processing Details

• —Source Dataset: ggosline/fdg-vol18-images
• —Model: nanonets/Nanonets-OCR2-3B
• —Model Size: 3.75B parameters
• —Number of Samples: 194
• —Processing Time: 17.8 minutes
• —Processing Date: 2026-02-25 23:50 UTC

Configuration

• —Image Column: image
• —Output Column: markdown
• —Dataset Split: train
• —Batch Size: 16
• —Max Model Length: 8,192 tokens
• —Max Output Tokens: 15,000
• —GPU Memory Utilization: 80.0%

Model Information

Nanonets-OCR2-3B is a state-of-the-art document OCR model that excels at:

• —📐 LaTeX equations - Mathematical formulas preserved in LaTeX format
• —📊 Tables - Extracted and formatted as HTML
• —📝 Document structure - Headers, lists, and formatting maintained
• —🖼️ Images - Captions and descriptions included in <img> tags
• —☑️ Forms - Checkboxes rendered as ☐/☑
• —🔖 Watermarks - Wrapped in <watermark> tags
• —📄 Page numbers - Wrapped in <page_number> tags
• —🌍 Multilingual - Supports multiple languages

Dataset Structure

The dataset contains all original columns plus:

• —markdown: The extracted text in markdown format with preserved structure
• —inference_info: JSON list tracking all OCR models applied to this dataset

Usage

from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("{{output_dataset_id}}", split="train")

# Access the markdown text
for example in dataset:
    print(example["markdown"])
    break

# View all OCR models applied to this dataset
inference_info = json.loads(dataset[0]["inference_info"])
for info in inference_info:
    print(f"Column: {{info['column_name']}} - Model: {{info['model_id']}}")

Reproduction

This dataset was generated using the uv-scripts/ocr Nanonets OCR2 script:

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr2.py \
    ggosline/fdg-vol18-images \
    <output-dataset> \
    --model nanonets/Nanonets-OCR2-3B \
    --image-column image \
    --batch-size 16 \
    --max-model-len 8192 \
    --max-tokens 15000 \
    --gpu-memory-utilization 0.8

Performance

• —Processing Speed: ~0.2 images/second
• —GPU Configuration: vLLM with 80% GPU memory utilization

Generated with 🤖 UV Scripts