Azzindani/Invoices_Data

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📄 Invoice Image Dataset (N8N-Friendly Streaming Format)

This repository contains a large-scale collection of invoice data stored in a simple, sequential, machine-friendly structure designed specifically for workflow automation tools such as n8n, Airflow, Make.com, and other ETL/automation systems.

The dataset is formatted to allow easy, reliable, and high-performance pulling of individual invoice files one-by-one, without needing to download the entire dataset.

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📁 Directory Structure

All invoice data are stored inside a single directory:

data_0001/
│
├── invoice_00000001.png
├── invoice_00000002.png
├── invoice_00000003.png
├── invoice_00000004.png
└── ...
data_0002/
│
├── invoice_00010001.png
├── invoice_00010002.png
├── invoice_00010003.png
├── invoice_00010004.png
└── ...
...

✅ Naming Format

Each invoice follows a fixed-length 8-digit numbering scheme:

invoice_00000000.format

• Zero-padded (06d or more) numbering supports millions of invoices without renaming.
• format can be .png, .jpg, .jpeg, .webp, depending on original source.
• Each file is independent and can be downloaded individually.

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🎯 Purpose of This Dataset

This dataset acts as a normalized, unified output repository for invoice data collected and standardized from multiple sources:

• Other Hugging Face datasets
• GitHub repositories
• Parquet datasets containing embedded data
• Third-party OCR datasets
• Custom pipelines

Each invoice is extracted, normalized, renamed, and uploaded in a consistent structure.

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🔌 Why This Format Is Perfect for n8n Workflows

Most automation tools (including n8n) struggle with:

• datasets stored in large compressed files (ZIPs, JSONL, Parquet)
• datasets where data must be decoded from arrays/blobs
• datasets requiring full-dataset downloads

This repo is intentionally built to support file-by-file streaming, so n8n can:

✔ Pull one file at a time

Example URL:

https://huggingface.co/datasets/<USER>/<REPO>/resolve/main/data_0001/invoice_00000042.png

✔ Process invoices sequentially

You can loop:

1 → 2 → 3 → 4 → ...

As long as the file exists, n8n continues; when it fails (404), it stops — perfect for paginated workflows.

✔ Avoid handling large downloads

Each invoice is an independent file.

✔ Use lightweight logic

You only need a simple incrementing counter in n8n.

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🧩 Example n8n Workflow Logic

1. Start with variable:

   index = 1
   

2. Construct file URL:

   url = https://huggingface.co/datasets/<USER>/<REPO>/resolve/main/<folder>/invoice_<index padded>.png
   

3. HTTP GET → process invoice

4. Increment index

5. Repeat until HTTP GET returns 404

6. Stop workflow automatically

This allows n8n to stream invoices from Hugging Face like a controlled queue.

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📦 What This Dataset Contains

• Only raw invoice data
• No labels
• No associated OCR text
• No metadata

This repository is intended specifically as an output target for standardized invoices — not as an annotated machine learning dataset.

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⚙️ How It Was Generated

A controlled pipeline:

1. Loads invoices from multiple formats (HF dataset, Parquet, GitHub, OCR outputs, etc.)
2. Extracts image data
3. Converts and normalizes to .png or .jpg
4. Saves to folder/invoice_XXXXXXXX.format
5. Uploads each file individually using the Hugging Face API
6. Logs progress so the job can resume at any index

Designed with:

• Kaggle execution compatibility
• Resume-safe operation
• Support for millions of invoices
• No Git requirements (uploads via HF API)

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📬 Contact / Usage Notes

This dataset is meant for:

• ETL automation
• Synthetic invoice processing pipelines
• AI model pre-processing
• OCR experiments
• Workflow system testing (n8n, make.com)

If you use this dataset in your workflow, feel free to reference or credit the repo.

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