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README.md

@@ -5,7 +5,7 @@ license: apache-2.0
 
 # 📄 Invoice Image Dataset (N8N-Friendly Streaming Format)
 
-This repository contains a large-scale collection of invoice images 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.
+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.
 
@@ -13,16 +13,24 @@ The dataset is formatted to allow **easy, reliable, and high-performance pulling
 
 ## 📁 Directory Structure
 
-All invoice images are stored inside a single directory:
+All invoice data are stored inside a single directory:
 
 ```
-images/
+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
@@ -41,11 +49,11 @@ invoice_00000000.format
 
 ## 🎯 Purpose of This Dataset
 
-This dataset acts as a **normalized, unified output repository** for invoice images collected and standardized from multiple sources:
+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 images
+* Parquet datasets containing embedded data
 * Third-party OCR datasets
 * Custom pipelines
 
@@ -58,7 +66,7 @@ Each invoice is extracted, normalized, renamed, and uploaded in a consistent str
 Most automation tools (including **n8n**) struggle with:
 
 * datasets stored in large compressed files (ZIPs, JSONL, Parquet)
-* datasets where images must be decoded from arrays/blobs
+* 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:
@@ -68,7 +76,7 @@ This repo is intentionally built to support **file-by-file streaming**, so n8n c
 Example URL:
 
 ```
-https://huggingface.co/datasets/<USER>/<REPO>/resolve/main/images/invoice_00000042.png
+https://huggingface.co/datasets/<USER>/<REPO>/resolve/main/data_0001/invoice_00000042.png
 ```
 
 ### ✔ Process invoices sequentially
@@ -100,7 +108,7 @@ You only need a simple incrementing counter in n8n.
 2. Construct file URL:
 
    ```
-   url = https://huggingface.co/datasets/<USER>/<REPO>/resolve/main/images/invoice_<index padded>.png
+   url = https://huggingface.co/datasets/<USER>/<REPO>/resolve/main/<folder>/invoice_<index padded>.png
    ```
 
 3. HTTP GET → process invoice
@@ -117,7 +125,7 @@ This allows n8n to **stream** invoices from Hugging Face like a controlled queue
 
 ## 📦 What This Dataset Contains
 
-* Only raw invoice images
+* Only raw invoice data
 * No labels
 * No associated OCR text
 * No metadata
@@ -133,7 +141,7 @@ 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 `images/invoice_XXXXXXXX.format`
+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