Delivery notes go by many names: delivery receipts, goods received notes, proof of delivery documents, packing slips. Regardless of what your organization calls them, they all serve the same purpose. They confirm what was actually delivered versus what was ordered. The gap between those two things is where errors, disputes, and revenue leakage live.
Warehouses, procurement teams, and third-party logistics providers process hundreds of delivery notes every day. Each one arrives in a different format from a different carrier or supplier. Some are printed. Some are handwritten on carbon-copy pads in the cab of a truck. Some are photographed on a phone at a loading dock. Extracting structured data from this mess manually is slow, error-prone, and expensive. That is where delivery note OCR software comes in.
This guide covers the six best tools for extracting data from delivery notes in 2026, with a focus on accuracy across formats, handling of handwritten annotations, and how easily the extracted data flows into your downstream systems.
The core challenge with delivery notes is format variation. Unlike invoices, which have moved toward some degree of standardization, delivery notes remain wildly inconsistent. Every carrier uses a different layout. Every supplier prints their own version. Field-service drivers often add handwritten notes about damaged goods, partial deliveries, or substituted items. A single warehouse that receives shipments from 50 suppliers might encounter 50 completely different delivery note formats in a single week.
Manual data entry from delivery notes creates a bottleneck at the receiving dock. Warehouse staff either spend hours keying in item descriptions, quantities, and delivery dates, or they skip the data entry entirely and lose visibility into what actually arrived. Both outcomes are costly. Keying errors lead to inventory mismatches. Skipped entry leads to disputes with suppliers that are impossible to resolve because nobody recorded what was actually in the shipment.
The downstream workflow matters just as much as the extraction itself. Once you pull data from a delivery note, you need to match it against the original purchase order to flag discrepancies: wrong quantities, missing items, unauthorized substitutions. The best delivery note OCR tools do not just read text off a page. They structure it so that three-way matching between POs, delivery notes, and invoices becomes possible without manual reconciliation.
Lido extracts data from delivery notes without templates, training, or manual configuration. You upload a delivery note in any format (printed, handwritten, photographed) and Lido returns structured data including item descriptions, quantities delivered, delivery dates, carrier information, recipient signatures, and condition notes. There is no setup step where you draw boxes around fields or label training samples. It works on the first document.
The strongest proof point for Lido's delivery note capabilities comes from Disney Trucking, which processes 360,000 handwritten driver tickets per year through Lido. These are not clean, printed documents. They are handwritten field tickets filled out by drivers in truck cabs, often on carbon-copy paper, sometimes smudged or partially illegible. Lido handles them at scale with accuracy that replaced a team of manual data entry staff.
Lido outputs extracted data directly into spreadsheets. That makes it easy to match delivery data against purchase orders in Excel or feed it into your ERP or warehouse management system. The platform includes 50 free pages per month, so you can test it on your actual delivery notes before committing.
PackageX focuses specifically on logistics document scanning, with particular strength in mailroom and receiving dock automation. The platform is built around package-level tracking, so it handles delivery notes as part of a broader receiving workflow that includes scanning shipping labels, matching packages to recipients, and logging deliveries in real time.
The tool works well for organizations where delivery note processing is part of a larger package intake operation: corporate mailrooms, university receiving departments, multi-tenant office buildings. PackageX captures delivery data through mobile scanning and integrates with common mailroom management systems. It is less suited to high-volume industrial receiving where delivery notes contain complex line-item detail like item descriptions, quantities, and lot numbers. The platform is optimized for package-level metadata rather than line-item extraction.
ABBYY Vantage is an enterprise intelligent document processing platform with decades of OCR technology behind it. For delivery note extraction specifically, its strengths are handling degraded scans, low-resolution photographs, and handwritten annotations. ABBYY supports over 200 languages, which matters for organizations that receive shipments from international suppliers whose delivery notes may include non-Latin characters or multilingual field labels.
Vantage uses pre-built document skills that can be customized for specific delivery note formats. The enterprise licensing model means it is best suited for large organizations with high volumes. Implementation typically involves a professional services engagement to configure the platform for your specific document types. For mid-market companies that process a few hundred delivery notes per month, the setup cost and timeline may not justify the investment compared to template-free alternatives.
Google Document AI provides cloud-based machine learning extraction that handles a wide range of document types, including logistics documents. The platform offers both general-purpose document parsing and specialized processors for specific document categories. For delivery notes, you would typically use the general form parser or train a custom processor on your specific formats.
The main advantage is integration with the Google Cloud ecosystem. If your organization already runs on GCP, Document AI slots into your existing infrastructure with minimal friction. Pay-per-page pricing keeps costs predictable. The main drawback is that strong accuracy on delivery notes typically requires a custom processor, which means collecting and labeling a training dataset of your specific delivery note formats before the system performs well. Out-of-the-box accuracy on varied delivery note layouts tends to lag behind tools built specifically for logistics documents.
Nanonets takes a trainable ML approach to document extraction. You upload sample delivery notes, label the fields you want extracted, and train a model specific to your document formats. Once trained, the model handles documents that match the patterns it learned. Nanonets includes workflow automation features like approval routing and database integration that help move extracted data into downstream systems.
The training approach works well if your delivery notes come from a small number of suppliers with consistent formats. Train on 20 to 30 samples per format and accuracy gets high quickly. The limitation shows up when you receive delivery notes from many different sources. Each new format requires its own training cycle, and the model does not generalize well to layouts it has not seen before. Pricing starts at $499 per month for the professional tier, which includes the training infrastructure and automation workflows.
Extend.ai positions itself as a logistics document specialist, built for the document types that move through supply chain operations. The platform handles bills of lading, packing lists, delivery notes, and other shipping documents as core use cases rather than afterthoughts bolted onto a general-purpose OCR engine.
The logistics-specific focus means Extend.ai understands the relationships between fields on supply chain documents. It knows that a delivery note references a specific BOL number, that quantities should reconcile against a packing list, and that carrier codes map to specific logistics providers. This domain awareness reduces the post-extraction cleanup that general-purpose tools often require. The platform is relatively new compared to established players like ABBYY, so the long-term track record is shorter. Still, the specialization makes it worth evaluating if your extraction needs are primarily logistics-focused.
Extracting data from delivery notes is only the first step. The real value comes from matching that data against the original purchase orders to identify discrepancies before they cascade into invoice disputes. When a delivery note says 480 units were delivered but the PO was for 500, you want to flag that immediately. You do not want to discover it three weeks later when the supplier sends a full-quantity invoice and your AP team cannot figure out why the numbers do not match.
The most efficient workflow is to extract delivery note data directly into a spreadsheet or structured format where it can be compared against PO data programmatically. Lido makes this easy by outputting delivery note data into spreadsheet rows that can be matched against PO data already in Excel. For organizations running OCR across their logistics operations, connecting delivery note extraction to PO matching eliminates the manual reconciliation step that typically absorbs hours of procurement team time every week.
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.
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