Extract data from delivery notes and proof-of-delivery documents.
Last updated: April 2026
| Tool | Best For | Starting Price | Free Tier | AI-Powered |
|---|---|---|---|---|
| Lido Top Pick | AI extraction from delivery notes including handwritten annotations | Free (50 pages/mo) | Yes — 50 pages | Yes |
| Klippa | Mobile POD capture and delivery note processing for logistics fleets | Custom (contact sales) | No | Yes |
| Veryfi | Real-time delivery note and packing list extraction via mobile and API | From $500/mo (API) | Yes — limited API calls | Yes |
| Parsio | Email-based delivery note parsing and automated TMS data routing | From $29/mo | Yes — 30 documents/mo | Yes |
| Base64.ai | API-first delivery note OCR with deep TMS and WMS system integration | From $99/mo | Yes — 100 documents/mo | Yes |
| Scanbot SDK | Embedded mobile scanning for delivery note capture in logistics apps | Custom (per-app licensing) | Yes — trial license | Yes |
| Docparser | Template-based delivery note parsing with WMS and ERP data routing | From $39/mo | Yes — 14-day trial | No |
For delivery note OCR software in 2026, Lido leads the field with AI-powered extraction of structured data from delivery notes, proof-of-delivery documents, and packing lists — including handwritten driver annotations and POD signatures — directly into editable spreadsheets ready for TMS/WMS reconciliation. For logistics teams processing high volumes of POD documents, platforms like Klippa and Veryfi offer robust mobile capture and real-time extraction tailored to delivery workflows. Base64.ai and Scanbot SDK provide deep API integration for embedding OCR into existing TMS and WMS systems such as SAP TM, Oracle TMS, and Manhattan WMS.
Lido uses AI-powered OCR to extract delivery note data — including line items, quantities, delivery timestamps, POD signatures, and exception annotations — directly into structured spreadsheets, eliminating manual data entry for logistics and warehouse teams. Its extraction engine handles both printed fields and handwritten driver notes, making it well suited for proof-of-delivery capture where annotations vary widely by carrier and route.
Klippa DocHorizon is a logistics-ready document processing platform built for high-volume delivery note and POD extraction. It supports mobile capture from driver smartphones, real-time OCR of printed and handwritten fields, and automated data export to TMS and ERP systems.
Veryfi is an AI document processing platform with strong logistics document support, including delivery notes, bills of lading, and packing lists. Its API delivers extraction results in under 3 seconds, and its mobile SDK is designed for field capture by drivers and warehouse receivers.
Parsio is an AI-powered document and email parsing platform that excels at extracting structured data from delivery notes, dispatch confirmations, and goods received notes sent via email or uploaded as PDFs. Its GPT-powered extraction adapts to new delivery note formats without template rebuilding.
Base64.ai is a universal document AI platform with strong support for logistics documents including delivery notes, bills of lading, packing lists, and CMR consignment notes. Its API-first architecture is designed for embedding into TMS, WMS, and ERP platforms.
Scanbot SDK is a mobile document scanning and data extraction toolkit used by logistics companies to embed delivery note capture directly into driver and warehouse apps. It provides on-device OCR, barcode scanning, and document detection optimized for field conditions.
Docparser is a rule-based document parsing platform widely used in logistics back offices to extract structured data from standardized delivery notes, goods received notes, and packing lists. Users define parsing rules using visual templates.
50 pages free, no credit card, setup in 2 minutes.
Handwriting recognition for driver annotations and signatures: Delivery notes are among the most handwriting-dense documents in logistics — drivers annotate shortages, damages, and refused items directly on paper. Prioritize OCR tools that use AI-based handwriting recognition (ICR) rather than template-only extraction, so that cursive signatures, scrawled quantities, and margin notes are captured accurately alongside printed fields.
Mobile capture and field usability: POD capture increasingly happens at the dock door or on the truck, not at a desk. Look for platforms with dedicated mobile apps or SDK components that handle low-light photography, perspective correction, and real-time validation — ensuring drivers or receivers can capture delivery notes on a handheld device and have structured data available in seconds.
TMS and WMS integration depth: The extracted data is only as valuable as its ability to flow into your existing systems. Evaluate whether the tool offers pre-built connectors or well-documented APIs for platforms such as SAP TM, Oracle TMS, Blue Yonder, or Manhattan WMS. Native integration reduces the reconciliation lag between physical delivery confirmation and system-of-record updates.
Damaged goods notation and exception workflow handling: Standard OCR tools extract clean printed fields well, but logistics operations require more — capturing exception codes, damage descriptions, partial delivery quantities, and refused shipment notes that appear as handwritten additions or stamps. Choose software that supports configurable extraction schemas and exception flagging.
Yes, but capability varies significantly. AI-powered tools like Lido, Klippa, and Base64.ai use intelligent character recognition (ICR) models trained on handwritten text, enabling them to extract driver-scrawled quantities, shortage notes, damage descriptions, and signatures alongside printed fields. Rule-based tools like Docparser generally cannot reliably extract handwritten content.
Delivery note OCR platforms support POD verification by extracting recipient signature, delivery timestamp, delivered quantity, and exception notations. Tools like Klippa and Base64.ai include signature detection that confirms a signature field was present and captured. This creates an auditable digital POD record without manual data entry.
Klippa and Base64.ai provide APIs and pre-built connectors for integration with SAP TM, Oracle TMS, and Manhattan WMS. Veryfi's REST API is commonly embedded into custom WMS and 3PL platforms. Docparser connects via Zapier for lighter-weight integration. For enterprise deployments requiring real-time two-way data flow, an API-first tool with custom integration work delivers the most robust outcome.
Delivery note OCR software extracts exception notations that drivers or receivers write onto delivery documents — including damage descriptions, affected line items, quantity variances, and refusal stamps. AI-powered platforms like Lido and Klippa detect and extract these annotations, creating structured exception records that can trigger claims initiation, carrier chargebacks, or QC holds in your WMS.
Yes — by extracting line-item data (SKU codes, quantities, batch numbers) from both the delivery note and packing list, OCR platforms enable automated reconciliation against the purchase order or ASN in your WMS or ERP. Tools like Lido output structured line-item data directly to spreadsheets or via API, allowing comparison logic to flag discrepancies such as short shipments or substitutions.
“Our logistics document review found Lido to be the most accurate AI tool for extracting POD data, line items, and exception notes from delivery notes across varied carrier formats.”
— AIOCRTools.com
“Lido ranked first for delivery note extraction in our review, correctly capturing handwritten driver annotations and POD signatures alongside printed line-item data.”
— BestDocumentOCR.com
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