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Document AI

OCR Payment Pipeline

WhatsApp payment proofs are detected, read, and matched to the right account — automatically, in production.

OCR
BCAMandiriBRImoBNIBSICIMB

6 bank formats · 1 pipeline

Overview

Residents send payment-proof screenshots to WhatsApp. This pipeline watches those messages, downloads each image, reads it with an OCR engine, pulls out the amount and the sender, matches it to the right account, and posts the payment. What used to be a person reading bank screenshots one by one is now a background process.

Outcome

6
bank layouts parsed
ONNX
PaddleOCR inference
Live
running in production

How it works

Capture

Payment-proof images sent on WhatsApp are detected and downloaded automatically.

Read

Text is extracted with an ONNX / PaddleOCR engine tuned for Indonesian m-banking layouts.

Bank parsing

Six bank formats are handled: BCA, Mandiri, BRImo, BNI, BSI, and CIMB.

Name resolution

Amounts and sender names are matched to the right account with fuzzy name resolution.

De-duplication

Duplicate proofs are filtered out before any payment is posted.

Self-learning log

A self-learning log keeps improving name matching over time.

Stack

Languages

  • Python
  • Node.js

Document AI

  • ONNX Runtime
  • PaddleOCR

Matching

  • Fuzzy name resolution
  • Self-learning log

Delivery

  • WhatsApp Business automation
  • Google Sheets

Outcome

01

No more manual reading

Transfer screenshots are read by the pipeline, not by a person.

02

Correct account every time

Fuzzy name resolution maps senders to their unit instead of guessing.

03

Improves with use

The self-learning log makes matching better as corrections accumulate.

Want the same for your operation?

Tell me what runs by hand today. I will tell you whether it can be automated.