---
language: en
license: mit
task_categories:
- image-to-text
- text-recognition
tags:
- scene-text-recognition
- str
- ocr
- computer-vision
- multimodal
size_categories:
- 1M<n<10M
---

# Union14M-L-STR: Labeled Scene Text Recognition Dataset

## Dataset Description

Union14M-L-STR contains 4M labeled images collected from 14 public available datasets for Scene Text Recognition (STR). This dataset has been refined through several strategies including cropping and de-duplication.

### Key Features

- **4M labeled images** from 14 public datasets
- **Cropped images** using minimal axis-aligned bounding boxes
- **De-duplicated** to remove duplicate images
- **5 difficulty levels**: easy, medium, hard, challenging, normal
- **Benchmark splits** for 9 different challenges

### Dataset Structure

```
{
    "image": PIL.Image,
    "text": str,
    "difficulty": str,
    "source_dataset": str,
    "original_filename": str
}
```

### Splits

- **train**: Training data with different difficulty levels
- **valid**: Validation data
- **test**: Test data (same as validation for now)
- **benchmark_***: Various benchmark categories (artistic, curve, etc.)

### Source Datasets

The dataset combines images from 14 public datasets including:
- art_curve, art_scene, COCOTextV2, hier_curve, hier_scene
- IIIT-ILST, KAIST, LSVT, MLT19, MTWI, neocr_dataset
- OpenVINO, ReCTS, RCTW, TextOCR, Uber

### Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Bekhouche/Union14M-L-STR")

# Access different splits
train_data = dataset["train"]
valid_data = dataset["valid"]
test_data = dataset["test"]

# Example usage
for sample in train_data:
    image = sample["image"]
    text = sample["text"]
    difficulty = sample["difficulty"]
    # Process your data...
```

### Citation

If you use this dataset, please cite the original Union14M paper and acknowledge the source datasets.