This project presents an Automated Invoice Processing System developed to simplify, automate, and streamline invoice handling in a digital business environment. The system is capable of accepting invoice files in various formats, including image, PDF, and text documents. By leveraging Optical Character Recognition (OCR) and custom Python scripts, the system extracts vital information from these documents, such as invoice number, invoice date, purchase order number, vendor name, and total amount. Once extracted, the data is stored in a structured SQL database, allowing for easy access, tracking, and further analysis. To support scalability and ensure secure storage, original invoice files are also archived in cloud storage. The system integrates with Microsoft Outlook to automatically fetch emails containing invoice attachments, thereby reducing manual intervention and ensuring a continuous flow of invoice data into the system. A user-friendly React.js dashboard provides real-time access to stored invoice records and enables users to filter, search, and verify data based on specific criteria such as customer name, date, and status. This system reduces the need for manual data entry, minimizes the chances of errors, and enhances operational efficiency by automating repetitive tasks. It serves as a robust solution for organizations seeking a reliable and centralized platform to manage their invoice processing activities with improved accuracy, speed, and transparency.
Devi et al. (Tue,) studied this question.