| --- |
| license: apache-2.0 |
| size_categories: |
| - 1M<n<10M |
| language: |
| - ne |
| - hi |
| - mr |
| - sa |
| - pi |
| pretty_name: Devanagari Compiled Dataset (ShareGPT) |
| tags: |
| - ocr |
| - devanagari |
| - image-to-text |
| - sharegpt |
| - instruction-tuning |
| - nepali |
| - hindi |
| - marathi |
| - document-understanding |
| task_categories: |
| - image-to-text |
| - question-answering |
| --- |
| |
| # Dataset Card: Devanagari Compiled Dataset (ShareGPT) |
|
|
| ## Dataset Description |
|
|
| This is a **curated compilation of 7 public Devanagari OCR datasets**, filtered for script purity and repackaged into the **ShareGPT conversation format** for fine-tuning vision-language models like GLM-OCR. |
|
|
| The dataset is distributed as **42 compressed image batches** (to enable manageable downloads) alongside a **single consolidated JSON annotation file** — `devanagari_ocr.json`. No fixed train/validation/test splits are provided, allowing you to create your own stratified splits based on your specific downstream tasks. |
|
|
| **Total size**: ~58 GB (images) + annotations. |
|
|
| --- |
|
|
| ## Repository Structure |
|
|
| ``` |
| devanagari_ocr_dataset/ |
| ├── data/ |
| │ ├── images_batch_001.tar.gz |
| │ ├── images_batch_002.tar.gz |
| │ ├── ... |
| │ └── images_batch_042.tar.gz |
| ├── devanagari_ocr.json |
| └── README.md |
| ``` |
|
|
| - **`data/`**: Contains all 42 compressed tarballs. Each tarball holds a subset of the images (e.g., `images_batch_001/`, `images_batch_002/`, etc.) or a flat directory of image files. |
| - **`devanagari_ocr.json`**: The single annotation file containing all samples in ShareGPT format. It can be either a JSON array or JSONL (JSON Lines) format — loaders for both are provided below. |
| |
| --- |
| |
| ## Extraction Instructions |
| |
| ### Extract All Image Batches into a Single Folder |
| |
| Run the following commands to extract every tarball into a unified `images/` directory: |
| |
| ```bash |
| cd data |
| mkdir -p ../images |
| for tar in images_batch_*.tar.gz; do |
| echo "Extracting $tar ..." |
| tar -xzf "$tar" -C ../images/ |
| done |
| ``` |
| |
| After successful extraction, your root folder will look like: |
| |
| ``` |
| devanagari_ocr_dataset/ |
| ├── images/ |
| │ ├── 000001.jpg |
| │ ├── 000002.png |
| │ └── ... (all extracted image files) |
| ├── devanagari_ocr.json |
| └── README.md |
| ``` |
| |
| > **Important**: The image paths inside `devanagari_ocr.json` are relative (e.g., `"images/000001.jpg"`). After extraction, ensure the `images/` folder is in the same root directory as the JSON file so the paths resolve correctly. If your tarballs extract into subfolders (e.g., `batch_001/`), you may need to move all files up one level using `mv ../images/*/* ../images/` or update the JSON paths accordingly. |
| |
| --- |
| |
| ## Data Format (ShareGPT Schema) |
| |
| `devanagari_ocr.json` contains all samples. Each entry follows the ShareGPT conversation structure: |
| |
| ```json |
| { |
| "image": "images/000001.jpg", |
| "conversations": [ |
| { |
| "from": "human", |
| "value": "<image>\nText Recognition:" |
| }, |
| { |
| "from": "gpt", |
| "value": "स्वर्ग आफै" |
| } |
| ] |
| } |
| ``` |
| |
| - **`image`** (`str`): Relative path to the image file from the repository root. |
| - **`conversations`** (`list`): A list of turns. The `human` turn always includes the `<image>` token followed by `Text Recognition:`. The `gpt` turn contains the ground-truth transcription. |
| |
| --- |
| |
| ## Loading the Dataset in Python |
| |
| ### Step 1: Verify Extraction |
| Ensure all images are extracted into `./images/`. |
| |
| ### Step 2: Load Annotations |
| |
| `devanagari_ocr.json` may be a **JSON array** or **JSONL** format. The following loader handles both: |
|
|
| ```python |
| import json |
| from PIL import Image |
| import os |
| |
| def load_sharegpt_annotations(json_path): |
| with open(json_path, "r", encoding="utf-8") as f: |
| # Try parsing as a JSON array first |
| try: |
| data = json.load(f) |
| if isinstance(data, list): |
| return data |
| except json.JSONDecodeError: |
| pass |
| |
| # Fallback: treat as JSONL (one JSON object per line) |
| f.seek(0) |
| data = [] |
| for line in f: |
| line = line.strip() |
| if line: |
| data.append(json.loads(line)) |
| return data |
| |
| def load_sharegpt_samples(json_path, image_root="./images"): |
| annotations = load_sharegpt_annotations(json_path) |
| samples = [] |
| for item in annotations: |
| # The "image" field is relative to the root, e.g., "images/000001.jpg" |
| img_rel_path = item["image"] |
| # Extract just the filename in case the path includes "images/" already |
| img_filename = os.path.basename(img_rel_path) |
| img_abs_path = os.path.join(image_root, img_filename) |
| |
| # Fallback: if the path in JSON includes a directory, try that too |
| if not os.path.exists(img_abs_path): |
| img_abs_path = os.path.join(image_root, img_rel_path.replace("images/", "")) |
| |
| image = Image.open(img_abs_path).convert("RGB") |
| |
| # Extract prompt and response |
| human_turn = item["conversations"][0] |
| gpt_turn = item["conversations"][1] |
| |
| samples.append({ |
| "image": image, |
| "prompt": human_turn["value"], |
| "response": gpt_turn["value"] |
| }) |
| return samples |
| |
| # Load all samples |
| all_samples = load_sharegpt_samples("devanagari_ocr.json") |
| print(f"Total samples: {len(all_samples)}") |
| ``` |
|
|
| ### Step 3: Create Your Own Train/Val/Test Splits |
|
|
| Since no predefined splits exist, we recommend creating reproducible splits using `sklearn`: |
|
|
| ```python |
| from sklearn.model_selection import train_test_split |
| |
| # 80% train, 10% val, 10% test |
| train, temp = train_test_split(all_samples, test_size=0.2, random_state=42) |
| val, test = train_test_split(temp, test_size=0.5, random_state=42) |
| |
| print(f"Train: {len(train)}, Val: {len(val)}, Test: {len(test)}") |
| ``` |
|
|
| For domain-specific evaluation (e.g., Nepali-only), you can filter by source metadata if included, or use the language tags (some entries may have `metadata.language`). |
|
|
| --- |
|
|
| ## Source Compilation Details |
|
|
| | Source Repository | Domain | Languages | Notes | |
| | :--- | :--- | :--- | :--- | |
| | `gauravgiri/nepali-ocr-dataset` | Printed page OCR | Nepali | Main source for Nepali printed text | |
| | `Malathip72/devanagari-ocr-dataset` | Page OCR | Pali/Sanskrit | Useful for script generalization | |
| | `rockerritesh/devanagari_and_roman_digits` | Digit recognition | Nepali/Hindi/English | Mixed Devanagari/Latin numerals | |
| | `krutrim-ai-labs/IndicVisionBench` (OCR config) | Page OCR | Hindi, Marathi | High-quality page-level data | |
| | `darknight054/indic-mozhi-ocr` | Word OCR | Hindi, Marathi | Cropped word-level recognition | |
| | `c3rl/IIIT-INDIC-HW-WORDS-Hindi` | Handwritten word OCR | Hindi | Handwritten names and words | |
| | `Nayana-cognitivelab/NayanaBench` | Document layout / VQA | Hindi, Marathi, Sanskrit | Complex layouts, useful for RAG | |
|
|
| *Duplicates (e.g., `vishwam-101/devanagari-ocr-dataset`) were manually excluded to prevent data leakage.* |
|
|
| --- |
|
|
| ## Intended Uses |
|
|
| 1. **Fine-tuning GLM-OCR / other VLMs** on Devanagari script recognition. |
| 2. **Nepali / Hindi / Marathi document digitization** pipelines. |
|
|
| --- |
|
|
| ## Limitations and Biases |
|
|
| - **Geographic bias**: Primarily drawn from academic datasets; may not represent mobile-captured, low-quality, or highly cursive regional scripts. |
| - **Mixed numerals**: Digit datasets contain both Devanagari and Latin numerals. If you require pure Devanagari output, apply a regex filter during post-processing. |
| - **Pali/Sanskrit content**: The page-OCR subset includes Pali/Sanskrit, which has different lexical patterns than modern Nepali/Hindi — useful for script learning but not for language-specific semantic tasks. |
| - **Storage requirements**: ~58 GB extracted. Ensure sufficient disk space. |
| - **No fixed splits**: Users must create their own splits. We recommend a reproducible random seed (e.g., 42) for consistency across experiments. |
|
|
| --- |
|
|
| ## Evaluation Metrics |
|
|
| When evaluating your model, we recommend: |
|
|
| - **CER** (Character Error Rate) |
| - **WER** (Word Error Rate) |
| - **Exact Match** (for short strings like digits or isolated words) |
|
|
| Compute these on the `gpt` response field against your model's decoded output. |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this compiled dataset, please cite: |
|
|
| ```bibtex |
| @misc{devanagari_ocr_dataset, |
| author = {himalaya-ai}, |
| title = {Devanagari Compiled Dataset (ShareGPT)}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/himalaya-ai/devanagari_ocr_dataset} |
| } |
| ``` |
|
|
| Additionally, please cite the individual source datasets listed in the Source Compilation table based on their respective repository documentation. |
|
|
| --- |
|
|
| ## Contact & Feedback |
|
|
| For questions, issues, or extraction problems, please open an issue on the Hugging Face repository. Contributions and suggestions are welcome! |
