stckmn/ocr-output-Directive017-1761354073

Document OCR using Nanonets-OCR2-3B

This dataset contains markdown-formatted OCR results from images in stckmn/ocr-input-Directive017-1761354068 using Nanonets-OCR2-3B.

Processing Details

• Source Dataset: stckmn/ocr-input-Directive017-1761354068
• Model: nanonets/Nanonets-OCR2-3B
• Model Size: 3.75B parameters
• Number of Samples: 21
• Processing Time: 3.2 minutes
• Processing Date: 2025-10-25 01:05 UTC

Configuration

• Image Column: image
• Output Column: markdown
• Dataset Split: train
• Batch Size: 16
• Max Model Length: 8,192 tokens
• Max Output Tokens: 4,096
• 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 \
    stckmn/ocr-input-Directive017-1761354068 \
    <output-dataset> \
    --model nanonets/Nanonets-OCR2-3B \
    --image-column image \
    --batch-size 16 \
    --max-model-len 8192 \
    --max-tokens 4096 \
    --gpu-memory-utilization 0.8

Performance

• Processing Speed: ~0.1 images/second
• GPU Configuration: vLLM with 80% GPU memory utilization

Generated with 🤖 UV Scripts