himalaya-ai/devanagari_ocr_dataset

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.

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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.

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Extraction Instructions

Extract All Image Batches into a Single Folder

Run the following commands to extract every tarball into a unified images/ directory:

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.

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Data Format (ShareGPT Schema)

devanagari_ocr.json contains all samples. Each entry follows the ShareGPT conversation structure:

{
  "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.

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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:

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:

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).

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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.

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Intended Uses

1. Fine-tuning GLM-OCR / other VLMs on Devanagari script recognition.
2. Nepali / Hindi / Marathi document digitization pipelines.

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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.

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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.

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Citation

If you use this compiled dataset, please cite:

@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.

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Contact & Feedback

For questions, issues, or extraction problems, please open an issue on the Hugging Face repository. Contributions and suggestions are welcome!