Getting Started with Annotation
Proper annotation is crucial for training LayoutLMv3 to understand invoices. This guide walks you through the process of creating a high-quality dataset.
1. Collect Samples
Gather 50 diverse invoice samples representing different layouts, languages, and formats you expect to process.
2. Define Labels
Create a consistent label schema (e.g., vendor_name, invoice_date, total_amount) that covers all relevant fields.
3. Choose Tools
Select annotation tools like Label Studio, VGG Image Annotator, or custom solutions that support bounding boxes and text.
Annotation Process Details
Key Annotation Steps
- Draw bounding boxes around each relevant text element
- Assign the appropriate label to each box
- Include the OCR-extracted text content
- Mark relationships between elements (optional)
- Validate annotations for consistency
Common Invoice Fields to Annotate
Header Fields
- Invoice Number
- Invoice Date
- Due Date
- Vendor Name
Customer Info
- Customer Name
- Customer Address
- Customer ID
- Tax ID
Line Items
- Description
- Quantity
- Unit Price
- Line Total
Totals
- Subtotal
- Tax Amount
- Discount
- Total Amount
Tools & Resources
Annotation Tools
Sample Annotation Format
{
"image_path": "invoice_001.jpg",
"width": 2480,
"height": 3508,
"annotations": [
{
"label": "vendor_name",
"bbox": [320, 120, 800, 160],
"text": "ACME Corporation"
},
{
"label": "invoice_number",
"bbox": [1600, 120, 2000, 160],
"text": "INV-2023-0042"
},
{
"label": "invoice_date",
"bbox": [1600, 180, 2000, 220],
"text": "2023-06-15"
}
]
}
This JSON structure shows how annotated data should be formatted for LayoutLMv3 training.
Next Steps After Annotation
Training Process
- Split your dataset (70% train, 15% validation, 15% test)
- Configure LayoutLMv3 model parameters
- Start with a small learning rate (e.g., 5e-5)
- Monitor loss and accuracy metrics
- Adjust hyperparameters as needed
Evaluation Metrics
- Field-level F1 score: Precision and recall for each field type
- Exact match accuracy: Percentage of perfectly extracted fields
- Partial match accuracy: For numeric fields with small deviations
- OCR quality impact: Compare with ground truth OCR
Scaling Up
Once you validate the approach with your 50-sample dataset, consider:
- Creating annotation guidelines for your team
- Implementing quality control processes
- Exploring semi-supervised learning techniques
- Using data augmentation for layout variations
Ready to Start Annotating?
Get started with your invoice annotation project today and unlock the power of LayoutLMv3 for document understanding.
