🧾 Invoice Data Extractor (v2.0.0)

A desktop-based AI invoice processing engine that extracts structured, item-level invoice data from PDF and JPG invoices using a hybrid OCR + LLM pipeline, and exports it directly to Excel.

✔ Tested on 1000+ real-world invoices ✔ Achieved ~91% field-level accuracy ✔ Packaged as a single plug-and-play .exe

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🎯 Problem This Solves

Invoice data extraction is messy because:

• PDFs vary wildly in layout
• Many PDFs are scanned images
• Tables break traditional parsers
• OCR alone is not enough
• Rule-based systems don’t scale

This tool solves that by combining:

• Deterministic OCR & preprocessing
• LLM-based semantic understanding
• Structured output enforcement
• Excel-ready data flattening

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✨ What’s New in v2.0.0

This release is a major backend upgrade.

🔥 Major Improvements

• Groq API integration

  • Replaced Gemini with Groq (LLaMA-3.1-8B-Instant)
  • Faster inference, lower latency, better consistency

• Advanced OCR Pipeline

  • OpenCV preprocessing (denoise, CLAHE, thresholding)
  • High-DPI PDF to image conversion
  • Custom Tesseract OCR configuration

• Robust PDF Handling

  • pdfplumber text extraction first
  • Automatic OCR fallback if text is missing

• Flattened Excel Output

  • Each invoice item becomes one row
  • Invoice-level fields repeated per item
  • Suitable for accounting & analytics

• Append to Existing Excel

  • New invoices can be appended to existing files
  • No overwriting, no manual merges

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🧠 System Architecture

PDF / JPG
   │
   ├─► pdfplumber (text PDFs)
   │
   └─► OpenCV + Tesseract (scanned PDFs / images)
            │
            ▼
     Cleaned invoice text
            │
            ▼
     Groq LLM (LLaMA-3.1-8B)
            │
            ▼
     Structured JSON (validated)
            │
            ▼
     Flattened rows
            │
            ▼
     Excel (.xlsx)

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🧠 How the Extraction Works (Step-by-Step)

1️⃣ PDF / Image Ingestion

• PDFs are first parsed using pdfplumber
• If no usable text is found → OCR pipeline is triggered
• JPG files always go through OCR

2️⃣ OCR Enhancement (Key to Accuracy)

Each page/image undergoes:

• Grayscale conversion
• Noise removal
• CLAHE contrast enhancement
• Adaptive thresholding
• Custom Tesseract config for invoice symbols & numbers

This significantly improves OCR quality on:

• Scanned invoices
• Low-contrast documents
• Poorly printed bills

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3️⃣ LLM-Based Data Extraction (Groq)

Extracted text is sent to Groq with a strict, structured prompt that enforces:

• Exact text extraction (no hallucination)

• Fixed fields:

  • Company name
  • Invoice number
  • Invoice date
  • FSSAI number
  • Item-level details

• JSON-only response

• Clean numeric values

• Standardized date format

Model used: llama-3.1-8b-instant

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4️⃣ Response Cleaning & Validation

• Markdown fences removed
• JSON boundaries detected manually
• Invalid responses discarded
• Safe fallback handling for malformed outputs

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5️⃣ Data Flattening Logic

• Each invoice item becomes one Excel row
• Invoice-level fields are repeated per item
• If no items are found → invoice-level row is still created

This makes the output:

• Pivot-friendly
• BI-ready
• Accounting-friendly

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6️⃣ Excel Export (Append-Safe)

• If Excel file exists → data is appended
• Column mismatches are auto-handled
• Weights are converted to kilograms
• File is saved using openpyxl

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📊 Accuracy & Testing

• ✅ Tested on 1000+ real invoices

• 📈 Achieved ~91% accuracy

• Includes:

  • Printed PDFs
  • Scanned PDFs
  • Multi-item invoices
  • Inconsistent layouts

Accuracy measured on:

• Invoice number
• Date
• Item description
• Quantity
• Amount
• HSN codes (when present)

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🧪 Supported Input Formats

Format	Method
PDF (text)	pdfplumber
PDF (scanned)	OCR fallback
JPG	OCR

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📤 Output

Excel File (.xlsx)

Each row represents one invoice item.

Columns include:

• company_name
• invoice_number
• invoice_date
• fssai_number
• description
• hsn_code
• quantity
• weight (kg)
• rate
• amount

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🚀 Getting Started

🔽 Download Executable

👉 Download v2.0.0

No Python. No setup. Just run.

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🔑 Groq API Key (Required)

1. Create a Groq account
2. Generate an API key
3. Paste it when prompted by the app

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🛠 Usage Flow

1. Launch the .exe
2. Enter Groq API key
3. Select one or multiple PDF/JPG invoices
4. Choose output Excel file (new or existing)
5. Extraction starts automatically
6. Excel file is generated/appended

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🧪 Build From Source

git clone https://github.com/Cherry28831/Invoice-Data-Extractor.git
cd Invoice-Data-Extractor
pip install -r requirements.txt
cd backend
pyinstaller invoice-backend.spec
cd ..
npm start 
npm run dist

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🌱 Future Roadmap

• Vendor auto-classification
• GST / tax breakup extraction
• CSV & ERP exports
• Multi-language invoices
• Cloud + desktop hybrid mode

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🤝 Contributions

This project is actively evolving. Contributions, optimizations, and feedback are welcome.