Invoice Processing

Invoice Processing #

This document describes the invoice processing capabilities of the BonsAI platform, specifically focusing on the Bonsai Invoice component.

Overview #

Bonsai Invoice is a specialized service that extracts structured data from invoice documents. It uses a combination of computer vision, OCR, and machine learning to identify and extract key information from invoices.

Architecture #

The invoice processing system consists of several components:

  1. Document Preprocessing: Handles initial document processing (format conversion, optimization)
  2. OCR Engine: Extracts text from images
  3. Entity Recognition: Identifies key entities (invoice number, dates, amounts)
  4. Validation Engine: Validates extracted data
  5. Correction System: Provides mechanisms for user corrections

Processing Pipeline #

The invoice processing pipeline follows these steps:

  1. Document upload via API or webapp
  2. Document preprocessing and normalization
  3. OCR processing to extract text
  4. Entity extraction using ML models
  5. Validation of extracted data
  6. Storage of processed invoice data
  7. User review and correction workflow

Extracted Fields #

The system extracts the following fields from invoices:

  • Basic Information:

    • Invoice Number
    • Invoice Date
    • Due Date
    • PO Number

  • Financial Information:

    • Subtotal
    • Tax Amount
    • Total Amount
    • Currency

  • Parties:

    • Vendor Name
    • Vendor Address
    • Vendor Tax ID
    • Customer Name
    • Customer Address

  • Line Items:

    • Item Description
    • Quantity
    • Unit Price
    • Line Total

Machine Learning Components #

The system uses several ML models for different aspects of processing:

  • Document Classification: Identifies document type
  • Layout Analysis: Understands document structure
  • Entity Recognition: Identifies key fields
  • Relationship Extraction: Connects related information

Integration #

The invoice processing service integrates with other components:

  • API: RESTful API for programmatic access
  • Webapp: UI for user interaction
  • Database: Storage of processed data
  • Object Storage: Storage of original documents

Performance Metrics #

The system’s performance is measured using the following metrics:

  • Accuracy: Correctness of extracted fields
  • Processing Time: Time to complete processing
  • Error Rate: Rate of failed extractions
  • User Correction Rate: Frequency of user corrections

Development and Training #

The ML models are continually improved through:

  • Training Data Collection: Gathering diverse invoice samples
  • Model Training: Regular retraining with new data
  • Performance Evaluation: Monitoring model performance
  • Feedback Loop: Incorporating user corrections into training