Your question is Optimize OCR for Financial Documents. Take a moment with it on the right.
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You are working on an OCR pipeline for financial documents such as invoices, bank statements, receipts, and audit reports. The input is noisy, with skewed scans, stamps, low-resolution PDFs, merged tables, and frequent confusion between similar characters like 0 and O or 1 and l. The downstream NLP system needs clean text and reliable field extraction for amounts, dates, account numbers, and vendor names.
How would you optimize OCR for financial documents?