Name: Traqo.ai OCR POD
Brand: Traqo.ai
Rating: 4.8 (100 reviews)

Question 1

What is OCR proof of delivery (POD) software?

Accepted Answer

OCR proof of delivery software uses optical character recognition and computer vision to automatically extract key information from a physical POD document photographed in the field — consignment number, delivery date, quantity, recipient signature, stamp, condition notes, and other fields — and convert it into a structured digital record attached to the trip and invoice. Without OCR POD software, drivers carry physical PODs back to the depot, where a data entry team types each field manually — a process that takes days, introduces transcription errors, and delays the invoice matching and freight settlement cycle by one to four weeks.

Question 2

How does Traqo.ai's OCR POD capture work in the field?

Accepted Answer

The driver photographs the POD document using their regular WhatsApp or the Traqo.ai driver app immediately upon delivery. The image is uploaded to Traqo.ai's OCR engine, which applies document orientation correction, contrast enhancement, and a trained OCR model to extract all key fields. Extracted data is matched against the original indent and consignment record automatically. If extraction confidence is below the threshold on any field, a human reviewer is flagged to verify that specific field — the rest of the record is processed automatically. The complete digital POD is available in Traqo.ai within 60 seconds of the driver taking the photo.

Question 3

What accuracy rate does Traqo.ai's POD OCR achieve?

Accepted Answer

Traqo.ai's OCR engine achieves 99.4% extraction accuracy across the 1.2 million+ POD documents processed to date. The model is trained on a diverse dataset of POD formats used by carriers across Southeast Asia, South Asia, the Middle East, and East Africa — covering handwritten, printed, pre-formatted, and mixed-format documents in multiple languages. Accuracy is maintained even on low-quality photographs taken in poor lighting, at angles, or with partial obstruction, thanks to pre-processing steps that correct orientation and enhance contrast before the OCR model runs.

Question 4

What fields does Traqo.ai extract from a POD document?

Accepted Answer

Traqo.ai's OCR engine extracts the following standard fields from a POD: Lorry Receipt (LR) or Consignment Note number, delivery date and time, delivered quantity and weight, recipient name and signature, company stamp or seal, vehicle number, condition of goods at delivery (good condition, short, damaged), remarks, and transporter name. For customised POD formats with additional fields — temperature readings for cold chain, barcode or QR scan values, regulatory reference numbers — Traqo.ai can be configured to extract custom fields specific to your carrier's document template.

Question 5

How does digital POD speed up freight settlement?

Accepted Answer

Freight settlement requires three documents to be matched: the transporter invoice, the Lorry Receipt, and the proof of delivery. When POD is paper-based, the settlement cycle is blocked until physical documents are returned to the finance office — which can take 3–30 days depending on the carrier and route distance. With Traqo.ai's digital POD, the settlement-ready record is available within 60 seconds of delivery. The 3-way matching engine in Traqo.ai's Freight Settlement module can immediately match the digital POD against the existing indent and invoice, and flag clean matches for payment — enabling same-cycle settlement instead of month-end settlement.

Question 6

What happens if a POD photo is blurry or incomplete?

Accepted Answer

Traqo.ai's OCR engine includes automated image quality assessment that checks for blur, low contrast, obstruction, and missing document sections before processing. If the image quality falls below a threshold on critical fields, the driver is immediately prompted via WhatsApp to retake the photo with guidance on proper framing and lighting. If a usable image cannot be obtained (e.g., damaged document, customer declined to sign), the driver can add a structured exception note explaining the circumstances. This exception record is attached to the consignment and flagged for supervisor review, preventing the settlement cycle from being blocked indefinitely.

Question 7

Can Traqo.ai's OCR detect delivery damage or shortages?

Accepted Answer

Yes. Traqo.ai's computer vision model is trained to detect and flag visual indicators of damage or shortage in POD documents: crossed-out quantities, handwritten 'short delivery' or 'damaged' annotations, crossed signatures, condition stamps (damaged, broken, wet), and incomplete delivery notes. When any of these indicators are detected, the consignment is automatically tagged as a dispute candidate and routed to the customer service and finance teams for review. The digital image is preserved as evidence, eliminating the 'missing document' problem that delays most freight damage claims.

Question 8

Which POD document formats and languages does Traqo.ai support?

Accepted Answer

Traqo.ai's OCR engine supports printed, handwritten, and mixed-format POD documents in English, Hindi, Arabic, Bahasa (Indonesia and Malaysia), Thai, and several other languages used in key logistics markets. The model adapts to the specific POD template used by each carrier in your panel — including pre-formatted lorry receipt books, carbonless copy PODs, and plain-paper signed delivery notes. For carriers with non-standard or highly customised formats, Traqo.ai's team configures a carrier-specific extraction template during the integration onboarding process.

OCR POD

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WhatsApp capture

99% OCR accuracy

Damage detection

Searchable archive

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