A robust Financial Documents OCR solution, especially one powered by AI and Intelligent Document Processing (IDP), can accurately extract a comprehensive range of data fields essential for accounting, auditing, and financial management.
Key data fields typically extracted from Financial Documents include:
- Invoices/Bills:
- Vendor Details: Name, Address, Contact.
- Invoice Details: Invoice Number, Date of Issue, Due Date, Total Amount, Subtotal, Tax Amount.
- Line Items: Product/Service Description, Quantity, Unit Price, Line Total.
- Purchase Order (PO) Number, GL Codes.
- Bank Statements:
- Account Details: Account Number, Bank Name, Statement Period.
- Transaction Details (tabular): Date, Description, Debit/Withdrawal Amount, Credit/Deposit Amount, Running Balance.
- Opening/Closing Balances.
- Receipts:
- Merchant Name, Date of Purchase, Total Amount, Line Items, Tax, Payment Method.
- Purchase Orders (POs):
- Buyer/Seller Info, PO Number, Order Date, Delivery Date.
- Ordered Items (tabular): Product Codes, Description, Quantity, Unit Price.
- Financial Reports (e.g., Balance Sheets, Income Statements):
- Account Names/Categories (e.g., Assets, Liabilities, Revenue, Expenses).
- Corresponding Amounts (current period, prior period, year-to-date) in tabular format.
- Report Date, Company Name.
- Credit Applications/Loan Forms:
- Applicant Demographics, Income, Assets, Liabilities, Credit Score.
How AI Ensures Accuracy: Nanonets leverages sophisticated AI (ML, NLP, CV) models trained on vast datasets of global financial documents. This allows the AI to:
- Understand Context: Discern field meanings (e.g., distinguishing "a deposit" from "a withdrawal," or "a line item" from "a total amount") regardless of position or varying labels.
- Handle Layout Agnostically: Accurately extract data even if financial document layouts differ significantly (e.g., various bank statements, different vendor invoices).
- Process Scanned/Handwritten Data: Advanced OCR and Handwriting Text Recognition (HTR) enable extraction from image-based or partially handwritten documents with high accuracy.
- Extract Complex Tables: Precisely capture granular data from financial tables, even those without clear borders, with merged cells, or multi-line entries.
- Customization: Platforms like Nanonets allow users to easily train AI for unique financial document layouts or specific niche fields.
This granular and accurate data extraction transforms unstructured Financial Documents into structured, actionable information for seamless integration into ERP and accounting systems.
