Eight fields per invoice in 5 days: how LabelFort annotated 4,260 invoice images at scale

The client builds document intelligence workflows and OCR model training pipelines that require precise, label-aligned bounding boxes at production volume. Their invoice automation initiatives depend on tight value-only capture across thousands of images - with field definitions strict enough that distributed annotator teams produce uniform training signal.

LabelFort processed 4,260 invoice images across eight core invoice fields - 34,080 labeled fields - within 5 days, using a single-pass workflow with standardized tight boxing rules across 32 annotators.

The challenge

Invoice images vary in layout, font, and field placement at scale. Annotators must draw one tight rectangle around each field value - excluding label words, covering multi-line addresses completely, and skipping unreadable text - while maintaining field mapping consistency across thousands of records.

The client needed high-precision bounding box delivery at enterprise volume within a 5-day window.

• Tight boxes around target values only - label words excluded unless inseparable
• Multi-line addresses captured in one complete bounding box
• Unreadable text skipped to avoid noisy labels
• Correct field mapping across eight structured fields
• Consistent boxing behavior across 32 annotators on 4,260 varied templates

What we delivered

Each invoice image was annotated using tight rectangular bounding boxes around the text for the following fields:

1. Invoice Number
2. Invoice Issue Date
3. Name of Seller/Vendor
4. Address of Seller/Vendor
5. Contact Number of Seller/Vendor
6. Name of Client/Buyer
7. Address of Client/Buyer
8. Total Amount (in numbers)

The annotation goal was a tightly fitted rectangular bounding box with the correct label assigned, producing model-ready training data for invoice extraction.

Our approach

LabelFort enforced a single-pass workflow with global quality rules and standardized field definitions across the full annotator team.

• One tight rectangular bounding box per field - label words excluded unless inseparable.
• Multi-line values such as addresses captured in one box covering the full text block.
• Boxes kept tight and complete - no cut-off characters or excessive whitespace.
• Unreadable text skipped rather than annotated with noisy labels.
• Standardized field definitions and consistency checks applied across the team.

Results

The engagement delivered a high-precision invoice bounding box dataset at production scale - tight boxing rules applied consistently across the full dataset.

One reporting note: the label total counts the eight-field framework applied across all 4,260 images (4,260 × 8 = 34,080 labeled fields). Values that were unreadable were skipped per guideline rather than boxed, so the delivered box count is the annotated subset of that total.

• 4,260 invoice images annotated
• 34,080 labeled fields covered across eight invoice fields per image
• 176 hours 17 minutes of total agent effort
• 32 annotators on a single-pass workflow
• Consistent tight boxing rules applied across the full dataset

Key insights

• Tight value-only bounding boxes improve dataset quality for OCR and extraction models by reducing label noise.
• Clear rules around multi-line values improve consistency across diverse invoice templates.
• Field-level consistency ensures datasets remain production-ready for model training and evaluation.
• Distributed teams maintain high output quality when guided by standardized label definitions and boxing rules.

Impact

• OCR model training and evaluation for invoice field extraction
• Automated invoice processing workflows for finance and accounting operations
• Vendor and buyer identity resolution systems
• Payment reconciliation and reporting use cases

Key takeaways

• Covered 34,080 labeled fields across 4,260 invoice images in 5 days at production scale.
• Eight-field framework covered vendor, buyer, contact, and total amount extraction.
• Value-only boxing rules excluded label words and standardized multi-line address capture.
• Standardized field definitions held 32 annotators to consistent quality across thousands of varied templates.