Invoice OCR Processing System

Results

The Challenge

A fruit export company was spending hours every day manually processing invoices from suppliers and logistics partners. Staff were re-keying line items, weights, quantities, and totals from PDFs and scanned documents into their accounting system. The process was error-prone, slow, and a significant bottleneck during peak export seasons when invoice volumes surged.

Our Solution

We built a fully automated invoice processing pipeline with a human-in-the-loop web application that gives the team full visibility and control:

• Automatic ingestion of invoices from email attachments and uploaded files
• AI-powered OCR extraction of line items, quantities, weights, unit prices, and totals
• Intelligent field mapping that adapts to different supplier invoice formats
• Confidence scoring on every extracted field with visual highlighting
• Web-based review interface where staff approve, correct, or flag entries before export
• One-click export of validated data in ERP-ready format

Technical Architecture

• Document preprocessing pipeline handling PDFs, scans, and photos
• OCR engine with layout analysis for table and line-item detection
• AI post-processing for field classification and validation
• Postgres database with full audit trail of every extraction
• React web application with side-by-side document and data views
• Role-based access for operators, reviewers, and admins

Key Challenges Solved

• Supplier invoices varied wildly in format — trained adaptive extraction models per supplier
• Scanned documents had inconsistent quality — built preprocessing pipeline with deskew, contrast, and noise reduction
• Staff needed confidence to trust automation — built review UI with confidence scores and side-by-side comparison
• Peak season volume spikes — system scales horizontally with queue-based processing

Tech Stack