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test3-ocr-output

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ocr
document-processing
paddleocr-vl
uv-script
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[{"model_id": "PaddlePaddle/PaddleOCR-VL", "model_name": "PaddleOCR-VL", "model_size": "0.9B", "task_mode": "ocr", "column_name": "paddleocr_ocr", "timestamp": "2026-02-06T17:26:26.734330", "temperature": 0.0, "max_tokens": 4096, "smart_resize": true}]

Document Processing using PaddleOCR-VL (OCR mode)

This dataset contains OCR results from images in minhpvo/test3-ocr-input using PaddleOCR-VL, an ultra-compact 0.9B OCR model.

Processing Details

Configuration

  • Image Column: image
  • Output Column: paddleocr_ocr
  • Dataset Split: train
  • Batch Size: 16
  • Smart Resize: Enabled
  • Max Model Length: 8,192 tokens
  • Max Output Tokens: 4,096
  • Temperature: 0.0
  • GPU Memory Utilization: 80.0%

Model Information

PaddleOCR-VL is a state-of-the-art, resource-efficient model tailored for document parsing:

  • 🎯 Ultra-compact - Only 0.9B parameters (smallest OCR model)
  • 📝 OCR mode - General text extraction
  • 📊 Table mode - HTML table recognition
  • 📐 Formula mode - LaTeX mathematical notation
  • 📈 Chart mode - Structured chart analysis
  • 🌍 Multilingual - Support for multiple languages
  • ⚡ Fast - Quick initialization and inference
  • 🔧 ERNIE-4.5 based - Different architecture from Qwen models

Task Modes

  • OCR: Extract text content to markdown format
  • Table Recognition: Extract tables to HTML format
  • Formula Recognition: Extract mathematical formulas to LaTeX
  • Chart Recognition: Analyze and describe charts/diagrams

Dataset Structure

The dataset contains all original columns plus:

  • paddleocr_ocr: The extracted content based on task mode
  • 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 extracted content
for example in dataset:
    print(example["paddleocr_ocr"])
    break

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

Reproduction

This dataset was generated using the uv-scripts/ocr PaddleOCR-VL script:

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \
    minhpvo/test3-ocr-input \
    <output-dataset> \
    --task-mode ocr \
    --image-column image \
    --batch-size 16 \
    --max-model-len 8192 \
    --max-tokens 4096 \
    --gpu-memory-utilization 0.8

Performance

  • Model Size: 0.9B parameters (smallest among OCR models)
  • Processing Speed: ~0.01 images/second
  • Architecture: NaViT visual encoder + ERNIE-4.5-0.3B language model

Generated with 🤖 UV Scripts

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