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Document OCR using dots.mocr

This dataset contains OCR results from images in davanstrien/encyclopaedia-britannica-1771 using dots.mocr, a 3B multilingual model with SOTA document parsing and SVG generation.

Processing Details

Configuration

  • Image Column: image
  • Output Column: markdown
  • Dataset Split: train
  • Batch Size: 16
  • Prompt Mode: ocr
  • Max Model Length: 24,000 tokens
  • Max Output Tokens: 24,000
  • GPU Memory Utilization: 90.0%

Model Information

dots.mocr is a 3B multilingual document parsing model that excels at:

  • 100+ Languages — Multilingual document support
  • Table extraction — Structured data recognition
  • Formulas — Mathematical notation preservation
  • Layout-aware — Reading order and structure preservation
  • Web screen parsing — Webpage layout analysis
  • Scene text spotting — Text detection in natural scenes
  • SVG code generation — Charts, UI layouts, scientific figures to SVG

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 dots.mocr script:

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
    davanstrien/encyclopaedia-britannica-1771 \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --prompt-mode ocr \
    --max-model-len 24000 \
    --max-tokens 24000 \
    --gpu-memory-utilization 0.9

Generated with UV Scripts

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