We tested the leading tools on the market to create this list of the best delivery note OCR software in 2026. Read on to discover our top picks.
The best delivery note OCR software in 2026 is Lido. It extracts line items, quantities, and shipping details from any delivery note format with the highest accuracy.
The best delivery note extraction tool. Lido handles the format diversity across carriers and suppliers.
A solid logistics-focused option for mailroom and receiving automation.
The strongest OCR for degraded delivery notes. Requires significant investment.
A capable API for engineering teams building custom delivery note extraction.
A decent option for teams with standardized delivery note formats.
A newer logistics-focused extraction option. Less established than competitors.
Join hundreds of teams growing faster by automating the busywork with Lido.
Delivery note OCR ranges from general extraction tools to logistics-specific platforms. Choose based on your document diversity and technical resources.
Format diversity. Delivery notes come in every format from handwritten slips to structured digital documents. Lido handles all formats. Template-based tools need setup per format.
Integration needs. PackageX integrates with mailroom workflows. Google Document AI requires custom development. Lido outputs to spreadsheets for universal import.
Budget. Lido offers a free tier. Nanonets has per-page pricing. Google charges pay-as-you-go. Everything else is custom priced.
Now that you know the strengths of each tool, you can choose the one that fits your receiving workflow.
Delivery note OCR can extract item descriptions, quantities delivered, delivery dates, carrier and driver information, recipient names and signatures, condition notes, reference numbers such as PO numbers and BOL numbers, and shipper and consignee addresses. The specific fields depend on the tool and the delivery note format, but most modern OCR platforms can capture both printed and handwritten text from standard delivery note layouts.
Yes, but accuracy varies widely between tools. Lido and ABBYY Vantage have the strongest handwriting recognition capabilities among the tools reviewed here. Lido processes over 360,000 handwritten driver tickets per year for a single customer, which demonstrates production-grade handwriting accuracy. General-purpose OCR engines like Google Document AI can read handwriting but typically require custom training to achieve high accuracy on messy field documents.
Extract your delivery note data into a structured format like a spreadsheet, then match key fields (item codes, quantities, PO reference numbers) against your purchase order records. The most common approach is to use a shared reference number such as the PO number that appears on both documents. Flag any rows where the delivered quantity does not match the ordered quantity, where items appear on one document but not the other, or where item descriptions differ. Tools that output to spreadsheets make this matching step easier to automate than tools that output to proprietary dashboards.
A bill of lading is a legal contract between a shipper and carrier that describes what is being transported and serves as a receipt for the shipment. A delivery note is a confirmation document that describes what was actually received at the destination. Bills of lading travel with the shipment. Delivery notes are generated or signed at the point of delivery. In a complete receiving workflow, you might OCR both documents: the bill of lading when the shipment arrives and the delivery note once the goods are inspected and accepted.
Pricing ranges from free tiers to enterprise contracts. Lido offers 50 free pages per month with paid plans that scale by volume. Nanonets starts at $499 per month for its professional tier. Google Document AI charges per page processed through its cloud API. ABBYY Vantage and PackageX typically require contacting sales for enterprise pricing. For most organizations that process under 1,000 delivery notes per month, a tool with a free tier or pay-per-page pricing will be more cost-effective than an enterprise platform with a flat annual license.