Best Bill of Lading OCR Software in 2026

Extract data from bills of lading, waybills, and shipping docs.

Last updated: April 2026

Quick Comparison

Tool Best For Starting Price Free Tier AI-Powered
Lido Top Pick Freight brokers, 3PLs, and carriers processing mixed-modal BOLs Free (50 pages/mo) Yes — 50 pages Yes
Descartes Enterprise 3PLs on the Descartes logistics platform Custom enterprise pricing No Yes
CargoWise Global freight forwarders and customs brokers Bundled with CargoWise One; volume-based pricing No Yes
Nanonets Engineering teams building custom BOL extraction pipelines $499/mo Free tier available Yes
ABBYY Vantage Enterprise document intelligence for complex BOL environments Custom enterprise pricing Trial available Yes
Azure AI Document Intelligence Logistics teams building Azure-native document pipelines From $1.50/1000 pages 500 free pages/mo Yes
Google Document AI GCP-native logistics document processing at scale From $0.0015/page at scale Trial credits Yes

The best bill of lading OCR software in 2026 is Lido, which delivers exceptional accuracy across every critical BOL field — from BOL number extraction and shipper/consignee/notify party capture to NMFC code parsing and freight charge classification. Lido handles ocean bills of lading, truck BOLs, air waybills, and rail waybills in a single workflow, making it the go-to solution for freight brokers, 3PLs, and carriers managing diverse shipment types. Its structured spreadsheet output maps directly to TMS workflows, and 50 free pages per month allows teams to validate accuracy before committing.

★ Editor's Choice — #1 Pick

1. Lido

★★★★★ 4.9/5

Lido's AI-powered BOL OCR engine captures carrier SCAC codes, container numbers, and seal numbers with the precision intermodal and ocean freight operations demand, while simultaneously extracting freight charges, payment terms (prepaid, collect, or third-party), and piece count/weight/dimension data into clean, structured spreadsheet output. Its extraction pipeline recognizes commodity descriptions alongside their NMFC codes and handles multi-page, multi-modal BOLs without manual field mapping. With TMS-ready output for Oracle TMS, SAP TM, MercuryGate, and BluJay, Lido eliminates the re-keying bottleneck that slows freight operations.

AI-powered extraction — no templates or training needed
Works with any document type: invoices, receipts, bank statements, and more
Outputs directly to spreadsheet, ERP, or API
50 free pages — no credit card required
50 free pages No credit card Setup in 2 minutes

2. Descartes

4.3/5

Descartes embeds document capture within its Global Logistics Network, supporting ocean BOLs, air waybills, and customs docs in a unified pipeline. Carrier and port community integration enables automated document matching. Strong for enterprises already on the Descartes platform, but OCR capabilities are not available standalone.

Pros

  • Deep integration with Descartes routing, compliance, and visibility modules
  • Strong ocean and air freight document coverage
  • Established carrier network connections reduce manual lookup

Cons

  • OCR is platform-dependent and not available standalone
  • NMFC code extraction less specialized than dedicated freight OCR tools
Visit Descartes →

3. CargoWise

4.2/5

CargoWise's embedded document capture ties ocean BOL and house BOL field extraction directly to shipment records. Container and vessel data auto-populate from extracted BOL fields, and customs declaration fields are pre-filled from commodity data. Strong for ocean freight but truck BOL and NMFC extraction are less developed.

Pros

  • Eliminates double-entry between BOL capture and shipment records
  • Strong ocean freight and customs document workflow
  • Container and seal number capture feeds directly into port visibility

Cons

  • Requires full CargoWise subscription — no standalone access
  • Truck BOL and NMFC code extraction less developed
Visit CargoWise →

4. Nanonets

3.9/5

Nanonets offers a REST API for custom BOL field extraction with trainable models for shipper, consignee, and commodity schemas. Confidence scoring per field enables downstream validation. Flexible for teams with unique carrier formats, but requires developer resources and has no pre-built NMFC extraction.

Pros

  • Highly flexible for teams with unique carrier or document format requirements
  • Good model training UX for adding new BOL templates
  • Confidence scores support automated quality gates before TMS ingestion

Cons

  • No pre-built NMFC code extraction — requires custom model training
  • Container number check-digit validation must be implemented by developer
Visit Nanonets →

5. ABBYY Vantage

3.8/5

ABBYY Vantage offers pre-built shipping and logistics document skills with industry-leading handwritten field recognition (ICR) for paper truck BOLs. Multi-language support handles international ocean freight documents. Best for large enterprises with strict compliance needs and high handwritten BOL volumes.

Pros

  • Industry-leading handwritten field recognition (ICR) for paper truck BOLs
  • Robust validation rules for container numbers, SCAC codes, and weight fields
  • On-premise and cloud deployment options

Cons

  • Significant implementation investment — not suitable for small teams
  • NMFC code extraction requires custom skill configuration
Visit ABBYY Vantage →

6. Azure AI Document Intelligence

3.6/5

Azure AI Document Intelligence offers custom model training on carrier-specific BOL layouts with native integration into Azure Logic Apps and Power Automate. Table extraction handles multi-line commodity and weight fields. Requires Azure expertise and has no out-of-the-box NMFC or SCAC code awareness.

Pros

  • Strong ecosystem fit for Azure-first organizations
  • Table extraction handles multi-commodity BOL line items accurately
  • Pay-per-page pricing scales down for low-volume use cases

Cons

  • No out-of-the-box NMFC code or SCAC code awareness
  • EDI 211 output requires significant custom development
Visit Azure AI Document Intelligence →

7. Google Document AI

3.5/5

Google Document AI offers specialized processors for logistics documents with high-volume batch processing via Cloud Storage triggers. BigQuery integration enables freight spend and lane analytics on extracted BOL data. Strong at scale but requires engineering teams and has no pre-built NMFC or SCAC extractors.

Pros

  • Scales to very high page volumes with minimal latency
  • BigQuery integration enables freight spend analytics on extracted BOL data
  • Competitive per-page pricing at enterprise scale

Cons

  • No pre-built NMFC code, freight class, or SCAC code extractors
  • TMS integration requires custom middleware development
Visit Google Document AI →

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How to Choose the Best Bill of Lading OCR Software

The first dimension is multi-modal BOL support. Ocean BOLs include vessel name, voyage number, port of loading/discharge, and container numbers, while truck BOLs center on SCAC codes, PRO numbers, and NMFC classifications. A robust platform must recognize each document type without requiring separate templates.

Pay close attention to NMFC code and commodity description extraction. Freight classification drives rating, billing, and compliance. The best tools parse commodity line items with their associated class, sub-class, and description even when carriers abbreviate or reformat fields.

For intermodal operations, container number and seal number accuracy is non-negotiable. ISO container numbers follow a strict check-digit algorithm, and a single transposition error breaks track-and-trace lookups and detention/demurrage calculations.

Finally, assess TMS integration depth and EDI compatibility. Look for output mapping to Oracle TMS, SAP TM, MercuryGate, and BluJay, and confirm whether the platform supports EDI 211 (motor carrier BOL) and EDI 214 (shipment status) transactions. Verify how each tool handles handwritten BOL fields — driver signatures and pen-filled addresses require ICR beyond standard OCR.

Frequently Asked Questions

What is the difference between an ocean BOL and a truck BOL for OCR purposes?

Ocean BOLs include vessel name, voyage number, port of loading/discharge, container numbers (ISO check-digit format), seal numbers, and notify party fields. Truck BOLs center on carrier SCAC codes, PRO numbers, NMFC freight classification codes, and payment terms (prepaid, collect, third-party). Ocean BOLs tend to be more consistently formatted by major shipping lines, while truck BOLs vary widely by carrier with many still produced as handwritten or low-resolution scans. The best tools like Lido handle both in a single pipeline.

Can BOL OCR software accurately extract NMFC codes?

Yes, but accuracy varies significantly. NMFC codes are critical for freight rating — errors cause carrier invoice disputes and compliance issues. The best tools parse truck BOL commodity table structures where NMFC codes appear alongside freight class, commodity description, piece count, and weight. Lido's engine preserves the relationship between classification and weight data. Generic tools like Azure or Google Document AI require custom model training to recognize NMFC codes at all.

How accurately can OCR read container numbers and seal numbers?

ISO container numbers follow a strict format — four-letter owner code, six-digit serial, and check digit — allowing mathematical validation post-extraction. The best platforms apply check-digit validation as a quality gate, flagging failures for human review. Seal numbers are less structured and often handwritten, requiring ICR. A single container number transposition breaks port gate-out matching and detention/demurrage calculations.

How does BOL OCR integrate with TMS platforms?

Integration depends on your TMS: Oracle TMS and SAP TM accept structured data via REST API or flat-file imports; MercuryGate and BluJay offer API and EDI ingestion. Lido exports pre-formatted spreadsheets for direct TMS import. Developer tools like Nanonets or Azure require custom middleware for field mapping. Confirm whether EDI 211 (motor carrier BOL) generation is supported.

How do BOL OCR tools handle handwritten fields?

Handwritten piece counts, weight corrections, driver signatures, and manually recorded seal numbers require ICR (intelligent character recognition) rather than standard OCR. ABBYY Vantage leads on raw ICR accuracy. Lido applies AI-enhanced extraction with confidence scoring that routes low-confidence handwritten fields for human review. For high volumes of paper truck BOLs, configuring a review queue for flagged fields is a best practice.

What Other Review Sites Say

“Where Lido truly differentiates is in multi-modal BOL support: it processes ocean bills of lading, truck BOLs, air waybills, and rail waybills through a single extraction pipeline, correctly identifying document type and applying the appropriate field schema without requiring separate templates for each carrier or shipping line.”

AIOCRTools.com

“For logistics teams evaluating BOL OCR with TMS integration readiness as the primary criterion, Lido delivers structured spreadsheet output that maps directly to Oracle TMS, SAP TM, MercuryGate, and BluJay field schemas out of the box — eliminating the custom middleware that cloud-infrastructure tools require.”

BestDocumentOCR.com

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