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You send one image and get the extracted data back in the same response. The approve, review or reject decision stays on your side.

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The pipeline

1

Detect and straighten

The service locates the passbook in the image and auto-rotates skewed or upside-down photos.
2

Read the text

Two OCR models run in parallel on the name and account number; the better result is kept automatically.
3

Classify the image

A template matcher scores how closely the document resembles known Thai bankbooks (similarity_score). A separate classifier flags scans and screenshots (is_scan) and detects the form: paper passbook or electronic statement.
4

Respond

The API returns the extracted fields, the bank enum and the scores. Field definitions are in the API reference.

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Signals you get back

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Making a decision

Bankbooks returns signals, not a verdict: your system decides whether to approve, reject or send the case to a reviewer. What each signal means and the recommended thresholds are in the API reference; the order below turns them into one policy. Apply the checks in order; the first one that fires decides the outcome.
Don’t hard-reject on is_scan alone. The flag is advisory and can miss screenshots made outside a bank’s mobile app.

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Matching Thai names

The API reference explains why Thai names need approximate matching. In practice:
  • Compare against the Thai account_name, not account_name_en — the English name is a transliteration and its spelling varies between documents.
  • Strip honorifics such as นาย, นาง and นางสาว and extra whitespace before comparing.
  • Compare account numbers with hyphens and spaces removed.