---
tags:
- ocr
- text-extraction
- rolmocr
- uv-script
- generated
---

# OCR Text Extraction using RolmOCR

This dataset contains extracted text from images in [davanstrien/playbills-pdf-images-text](https://huggingface.co/datasets/davanstrien/playbills-pdf-images-text) using RolmOCR.

## Processing Details

- **Source Dataset**: [davanstrien/playbills-pdf-images-text](https://huggingface.co/datasets/davanstrien/playbills-pdf-images-text)
- **Model**: [reducto/RolmOCR](https://huggingface.co/reducto/RolmOCR)
- **Number of Samples**: 10
- **Processing Time**: 5.8 minutes
- **Processing Date**: 2025-08-04 17:08 UTC

### Configuration

- **Image Column**: `image`
- **Output Column**: `rolmocr_text`
- **Dataset Split**: `train`
- **Batch Size**: 16
- **Max Model Length**: 24,000 tokens
- **Max Output Tokens**: 16,000
- **GPU Memory Utilization**: 80.0%

## Model Information

RolmOCR is a fast, general-purpose OCR model based on Qwen2.5-VL-7B architecture. It extracts plain text from document images with high accuracy and efficiency.

## Dataset Structure

The dataset contains all original columns plus:
- `rolmocr_text`: The extracted text from each image
- `inference_info`: JSON list tracking all OCR models applied to this dataset

## Usage

```python
from datasets import load_dataset
import json

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

# Access the extracted text
for example in dataset:
    print(example["rolmocr_text"])
    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](https://huggingface.co/datasets/uv-scripts/ocr) RolmOCR script:

```bash
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/rolm-ocr.py \
    davanstrien/playbills-pdf-images-text \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --max-model-len 24000 \
    --max-tokens 16000 \
    --gpu-memory-utilization 0.8
```

## Performance

- **Processing Speed**: ~0.0 images/second
- **GPU Configuration**: vLLM with 80% GPU memory utilization

Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)