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ICDAR_mini Dataset
A balanced mini subset of the ICDAR (International Conference on Document Analysis and Recognition) dataset with 50 samples per language. Includes actual document images and ground truth OCR text.
Dataset Details
- Total Samples: 500
- Total Images: 500
- Languages: 10
- Arabic (50 samples)
- Bangla (50 samples)
- Chinese (50 samples)
- Hindi (50 samples)
- Japanese (50 samples)
- Korean (50 samples)
- Latin (50 samples)
- Mixed (50 samples)
- None (50 samples)
- Symbols (50 samples)
File Structure
βββ README.md
βββ icdar_mini_index.json # Dataset metadata
βββ icdar_mini_Arabic.json # Language-specific data
βββ icdar_mini_Bangla.json
βββ ...
βββ images/
βββ tr_img_00001.jpg
βββ tr_img_00002.jpg
βββ ... (500 image files)
Each language subset is stored as a separate JSON file, and corresponding images are in the images/ directory.
Data Format
Each sample is a row in the dataset with the following columns:
image: Document imageground_truth: OCR ground truth textlanguage: Language of the document (e.g., "Latin", "Arabic", etc.)sample_id: Unique identifier for the sample
Example sample:
{
"sample_id": "icdar_tr_img_07848",
"image_path": "images/tr_img_07848.jpg",
"ground_truth": "Text content...",
"metadata": {
"dataset": "ICDAR",
"language": "Latin",
"num_text_lines": 49,
"image_size": [3264, 2448]
}
}
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("kenza-ily/icdar-mini")
# Access a sample
sample = dataset["train"][0]
print(f"Image: {sample['image']}")
print(f"Ground Truth: {sample['ground_truth']}")
print(f"Language: {sample['language']}")
print(f"Sample ID: {sample['sample_id']}")
# Iterate through samples by language
for sample in dataset["train"]:
if sample['language'] == 'Latin':
print(f"{sample['sample_id']}: {sample['ground_truth'][:50]}...")
Citation
Please cite the original ICDAR dataset if you use this subset in your research.
License
This subset follows the original ICDAR dataset license.
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