Datasets:
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language: en
license: mit
task_categories:
- image-to-text
- text-recognition
- self-supervised-learning
tags:
- scene-text-recognition
- str
- ocr
- computer-vision
- multimodal
- self-supervised
size_categories:
- 10M<n<100M
---
# Union14M-STR: Combined Scene Text Recognition Dataset
## Dataset Description
Union14M-STR is a combined dataset containing both labeled and unlabeled images for comprehensive Scene Text Recognition (STR) training. It combines Union14M-L (4M labeled) and Union14M-U (10M unlabeled) datasets.
### Key Features
- **4M labeled images** from 14 public datasets
- **10M unlabeled images** for self-supervised learning
- **Combined training** supporting both supervised and self-supervised approaches
- **5 difficulty levels** for labeled data
- **Multiple data sources** for comprehensive coverage
### Dataset Structure
```
{
"image": PIL.Image,
"text": str or null,
"source": str,
"subset": str,
"has_label": bool
}
```
### Splits
- **train**: Combined labeled and unlabeled training data
- **valid**: Labeled validation data only
### Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("Bekhouche/Union14M-STR")
# Access different splits
train_data = dataset["train"]
valid_data = dataset["valid"]
# Filter by label availability
labeled_data = train_data.filter(lambda x: x["has_label"] == True)
unlabeled_data = train_data.filter(lambda x: x["has_label"] == False)
# Example usage
for sample in train_data:
image = sample["image"]
text = sample["text"] # May be None for unlabeled data
has_label = sample["has_label"]
if has_label:
# Supervised training
pass
else:
# Self-supervised training
pass
```
### Citation
If you use this dataset, please cite the original Union14M paper and acknowledge the source datasets.
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