Automated Invoice Processing System

Many companies still rely on manual data entry methods for managing their invoices. Some of these companies deal with a high volume of invoices in various formats daily, resulting in time-consuming processes and resource wastage. To address this issue, a proposal is made to implement an efficient automated invoice processing system using deep learning. This system aims to reduce workload and enhance productivity for companies. In addition, a comprehensive review and comparison of existing techniques and similar systems have been conducted to identify the most suitable solution for this scenario. The proposed work utilizes advanced deep learning computer vision techniques, a simple Convolutional Neural Network (CNN) based on RPN, and LeNet-5 is used to detect and classify text objects on invoice documents. This paper utilized scanned invoices to assess the system's performance. A dataset consisting of 1000 scanned English invoices from the Scanned Receipts OCR and Information Extraction (SROIE) dataset. The system will predict and extract specific regions such as invoice number, date, payer information, and total amount from the invoices. However, it has been observed that low-resolution and unclear invoices can negatively impact the accuracy of OCR (Optical Character Recognition) pattern-matching methods. To mitigate this issue, an image pre-processing method has been incorporated, which reduces image noise and corrects page skew to achieve better performance.