Optimizing Multi-Format Freight Invoices Automation

Client Profile

Our client is a leading global logistics and supply chain enterprise, managing thousands of shipments daily through a vast network of partners, carriers, warehouses, ports, and remote logistics hubs. The company regularly receives freight invoices and supporting documents in diverse formats, including scanned PDFs, email attachments, physical paperwork, and handwritten receipts. Due to their geographically distributed operations, maintaining processing speed and accuracy is critical to delivering service excellence. The client sought an advanced OCR-driven solution to streamline the extraction and digitization of freight invoices and related logistics documents.

Challenges

The client faced significant difficulties due to the wide variety of freight-related documents, including scanned PDFs, handwritten receipts, customs declarations, and proof-of-delivery slips, which complicated standardization efforts. Manual sorting and data entry by finance and operations teams slowed processing, especially during peak periods when invoice volumes surged. Additionally, discrepancies in accessorial charges and invoice line items often went undetected, leading to revenue losses, prolonged carrier disputes, and compliance risks. Limited internet connectivity in some remote operational zones further delayed timely verification and processing, compounding these inefficiencies.

Solutions

To overcome these challenges, we implemented Percepta Parser platform combined with edge computing devices such as Raspberry Pi and NVIDIA Jetson units deployed at ports, warehouses, and remote hubs. This enabled near-instantaneous, on-site digitization and extraction of data from multi-format documents, including freight invoices, bills of lading, purchase orders, and delivery confirmations. The solution’s custom template-based extraction accurately captured key fields like charges, dates, and shipment identifiers while robust encryption protocols ensured secure handling of sensitive information. The edge-based approach also mitigated connectivity issues, enabling efficient processing directly at distributed locations.

Results

The implementation of the automated OCR solution resulted in a dramatic improvement in freight invoice processing speed and accuracy. Manual sorting and error-prone data entry were significantly reduced, enabling faster financial closure cycles and proactive identification of discrepancies. The organization experienced lower risk of fraud and revenue leakage, improved compliance, and greater operational efficiency across its geographically dispersed network. Furthermore, the edge computing model ensured consistent performance in connectivity-challenged zones, supporting scalable and resilient document processing throughout the client’s logistics infrastructure.