Designing a WhatsApp-First OCR Pipeline with Human Review (Lessons from Fleet Bills)

说明与用途

用于车队管理的 WhatsApp OCR 流程，结合人工审核，降低手工录入和会计错误。

问题

In Indian trucking, drivers send photos of fuel, toll, weighbridge, and repair slips via WhatsApp, leading offices to retype hundreds of images monthly, risking costly ledger errors if OCR output is taken as fact.

结果

The pipeline reduces manual retyping, flags inconsistencies, and ensures only validated entries enter accounting, while allowing supervisors to pause automation and reviewers to see explicit reasons for review.

工作流

• 1. Intake: store original attachment with sender, timestamp, and channel; verify sender is known.
• 2. OCR & field extraction: run OCR supporting Devanagari and Latin, extract fields with per‑field confidence.
• 3. Validation & matching: perform arithmetic consistency, duplicate detection, vehicle/trip matching, and date sanity checks.
• 4. Gate: if all checks pass, auto‑post; otherwise enqueue for review with explicit reasons.
• 5. Review: human corrects or retries extraction; upon approval, post to operational/accounting record linked to original.

证据

Real‑world slips are hard: Mixed Hindi and English text, sometimes handwritten

来源

If your pipeline writes the extracted values straight into an accounting ledger, you’ll create confident‑looking wrong numbers.

来源

Only post automatically when every check passes. Anything else goes to a queue with explicit reasons.

来源