README.md

metadata

tags:
  - ocr
  - document-processing
  - hunyuan-ocr
  - multilingual
  - markdown
  - uv-script
  - generated

Document OCR using HunyuanOCR

This dataset contains OCR results from images in NationalLibraryOfScotland/Scottish-School-Exam-Papers using HunyuanOCR, a lightweight 1B VLM from Tencent.

Processing Details

• Source Dataset: NationalLibraryOfScotland/Scottish-School-Exam-Papers
• Model: tencent/HunyuanOCR
• Number of Samples: 100
• Processing Time: 9.8 min
• Processing Date: 2025-11-25 16:15 UTC

Configuration

• Image Column: image
• Output Column: markdown
• Dataset Split: train
• Batch Size: 1
• Prompt Mode: parse-document
• Prompt Language: English
• Max Model Length: 16,384 tokens
• Max Output Tokens: 16,384
• GPU Memory Utilization: 80.0%

Model Information

HunyuanOCR is a lightweight 1B VLM that excels at:

• 📝 Document Parsing - Full markdown extraction with reading order
• 📊 Table Extraction - HTML format tables
• 📐 Formula Recognition - LaTeX format formulas
• 📈 Chart Parsing - Mermaid/Markdown format
• 📍 Text Spotting - Detection with coordinates
• 🔍 Information Extraction - Key-value, fields, subtitles
• 🌐 Translation - Multilingual photo translation

Prompt Modes Available

• parse-document - Full document parsing (default)
• parse-formula - LaTeX formula extraction
• parse-table - HTML table extraction
• parse-chart - Chart/flowchart parsing
• spot - Text detection with coordinates
• extract-key - Extract specific key value
• extract-fields - Extract multiple fields as JSON
• extract-subtitles - Subtitle extraction
• translate - Document translation

Dataset Structure

The dataset contains all original columns plus:

• markdown: The extracted text in markdown format
• 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 HunyuanOCR script:

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/hunyuan-ocr.py \
    NationalLibraryOfScotland/Scottish-School-Exam-Papers \
    <output-dataset> \
    --image-column image \
    --batch-size 1 \
    --prompt-mode parse-document \
    --max-model-len 16384 \
    --max-tokens 16384 \
    --gpu-memory-utilization 0.8

Generated with UV Scripts