README.md

---
task_categories:
- image-to-text
- visual-question-answering
tags:
- ocr
- document-analysis
- multilingual
- vqa
- webdataset
size_categories:
- 100K<n<1M
configs:
  - config_name: ar
    data_files:
      - split: train
        path: ar/*.tar
  - config_name: de
    data_files:
      - split: train
        path: de/*.tar
  - config_name: es
    data_files:
      - split: train
        path: es/*.tar
  - config_name: fr
    data_files:
      - split: train
        path: fr/*.tar
  - config_name: it
    data_files:
      - split: train
        path: it/*.tar
  - config_name: ja
    data_files:
      - split: train
        path: ja/*.tar
  - config_name: ko
    data_files:
      - split: train
        path: ko/*.tar
  - config_name: ru
    data_files:
      - split: train
        path: ru/*.tar
  - config_name: sa
    data_files:
      - split: train
        path: sa/*.tar
  - config_name: th
    data_files:
      - split: train
        path: th/*.tar
  - config_name: zh
    data_files:
      - split: train
        path: zh/*.tar
---

# Nayana-DocOCR Global Annotated Dataset

## Dataset Description

This is a large-scale multilingual document OCR dataset containing approximately 400GB of images with comprehensive annotations across multiple global languages and English. The dataset is stored in **WebDataset** format using TAR archives for efficient streaming and processing.

### Available Language Subsets
- **Arabic** (ar): Available
- **German** (de): Available
- **Spanish** (es): Available
- **French** (fr): Available
- **Italian** (it): Available
- **Japanese** (ja): Available
- **Korean** (ko): Available
- **Sanskrit** (sa): Available
- **Thai** (th): Available
- **Chinese** (zh): Available

### Dataset Statistics
- **Available Languages**: 1
- **Total Size**: ~400GB
- **Format**: WebDataset (TAR archives)
- **Chunk Size**: 5GB per TAR file

### Dataset Structure

This dataset uses the WebDataset format where each sample is stored as separate files within TAR archives:

- **Images**: Document images in JPG format (PNG converted to JPG for optimization)
- **Metadata Files**: Separate text/JSON files for each field:
  - `XXXXXXXX.jpg`: The document image
  - `XXXXXXXX.image_id.txt`: Unique identifier for the image
  - `XXXXXXXX.font_used.txt`: Font information used in the document
  - `XXXXXXXX.regions.json`: Text regions with bounding boxes and OCR results
  - `XXXXXXXX.vqa.json`: Visual Question Answering annotations

### Usage

#### Loading with WebDataset library (Recommended for large datasets)

```python
import webdataset as wds
import json
from PIL import Image
import io

# Create a WebDataset from TAR files
dataset = wds.WebDataset("path/to/language/tarfiles/*.tar")

# Process the dataset
for sample in dataset:
    # Access image
    image_data = sample["jpg"]  # Raw image bytes
    image = Image.open(io.BytesIO(image_data))
    
    # Access metadata
    image_id = sample["image_id.txt"].decode('utf-8')
    font_used = sample["font_used.txt"].decode('utf-8')
    regions = json.loads(sample["regions.json"].decode('utf-8'))
    vqa_data = json.loads(sample["vqa.json"].decode('utf-8'))
    
    print(f"Image ID: {image_id}")
    print(f"Font: {font_used}")
    print(f"Regions: {len(regions)}")
    print(f"VQA entries: {len(vqa_data)}")
```

#### Loading with HuggingFace datasets library

```python
from datasets import load_dataset
import json

# Load specific language subset
dataset = load_dataset("webdataset", data_dir="hf://datasets/Nayana-cognitivelab/NayanaDocs-Global-45k-webdataset/fr", split="train")

# Or with streaming for memory efficiency
dataset = load_dataset("webdataset", data_dir="hf://datasets/Nayana-cognitivelab/NayanaDocs-Global-45k-webdataset/fr", split="train", streaming=True)

# Access data
for sample in dataset:
    image = sample["jpg"]  # PIL Image
    image_id = sample["image_id.txt"]  # string
    font_used = sample["font_used.txt"]  # string
    regions = json.loads(sample["regions.json"])  # parsed JSON
    vqa_data = json.loads(sample["vqa.json"])  # parsed JSON
```

#### Manual download and processing

```python
from huggingface_hub import hf_hub_download
import tarfile
import webdataset as wds

# Download a specific TAR file
tar_path = hf_hub_download(
    repo_id="Nayana-cognitivelab/NayanaDocs-Global-45k-webdataset",
    filename="bn/bn_00000.tar",
    repo_type="dataset"
)

# Process with webdataset
dataset = wds.WebDataset(tar_path)
for sample in dataset:
    # Process sample
    pass
```

### Performance Tips

1. **Streaming**: Use `streaming=True` for large datasets to avoid downloading everything at once
2. **WebDataset library**: Use the `webdataset` library directly for maximum performance
3. **Parallel processing**: WebDataset supports parallel processing and data pipeline optimization
4. **Selective loading**: Download only the language TAR files you need

### File Organization

```
repository/
├── ar/
│   ├── ar_00000.tar
│   ├── ar_00001.tar
│   └── ...
├── fr/
│   ├── fr_00000.tar
│   ├── fr_00001.tar
│   └── ...
└── README.md
```

### Metadata Schema

#### regions.json
```json
[
  {
    "bbox": {"xmin": 10, "ymin": 20, "xmax": 100, "ymax": 50},
    "english_text": "Original text",
    "translated_text": "Translated text",
    "layout_type": "paragraph",
    "region_id": 1
  }
]
```

#### vqa.json
```json
{
  "questions": [
    {
      "question": "What is the main topic?",
      "answer": "Document analysis",
      "type": "topic",
      "options": ["Analysis", "Summary", "Review"]
    }
  ]
}
```