metadata
license: cc-by-nc-4.0
language:
- en
tags:
- ocr
- text-recognition
- scene-text
- image-to-text
- 100k<n<1M
TokenShrink-OCR Dataset
Introduction
This is a large-scale dataset containing 120,000 images, designed for Optical Character Recognition (OCR) tasks.
All images are derived from the ImageNet database, providing a challenging collection of text against complex backgrounds, varied lighting conditions, and diverse fonts.
Dataset Structure
All image files are stored in a sharded structure.
All data (train, validation, test) has been split into small folders, each containing 1,000 files.
The directory structure in the remote repository is as follows:
|-- train/
| |-- 000/
| | |-- image_0000001.jpg
| | |-- image_0000002.jpg
| | `-- ... (1,000 files)
| |-- 001/
| | |-- image_0001001.jpg
| | `-- ... (1,000 files)
| |-- 002/
| | `-- ...
| `-- ... (e.g., up to "119")
|
|-- validation/
| |-- 000/
| | |-- image_val_00001.jpg
| | `-- ... (1,000 files)
| |-- 001/
| | `-- ...
| `-- ...
|
`-- test/
|-- 000/
| |-- image_test_00001.jpg
| `-- ... (1,000 files)
|-- 001/
| `-- ...
`-- ...
How to Use
You can easily load all sharded data using the datasets library, the imagefolder loader, and a glob (wildcard) pattern.
Install Dependencies
pip install datasets
The datasets library will automatically find all files matching the */*.jpg pattern and merge them into a single dataset.
```python
from datasets import load_dataset
REPO_ID = "LukB4UJump/TokenShrink-OCR"
IMAGE_EXTENSION = "jpg"
data_files = {
"train": f"{REPO_ID}::train/*/*.{IMAGE_EXTENSION}", # Matches train/000/*.jpg, train/001/*.jpg ...
"validation": f"{REPO_ID}::validation/*/*.{IMAGE_EXTENSION}",
"test": f"{REPO_ID}::test/*/*.{IMAGE_EXTENSION}"
}
# Use the "imagefolder" loader
# streaming=True allows you to access the data without downloading all 120k images, saving disk space
dataset = load_dataset(
"imagefolder",
data_files=data_files,
streaming=True # Recommended for large datasets
)
# --- If you want to download all data at once (requires sufficient disk space) ---
# dataset = load_dataset(
# "imagefolder",
# data_files=data_files,
# streaming=False
# )
print(dataset)