Tables
Transaction rows stay useful
LeapOCR is built to keep row-level transaction data readable and structured instead of flattening the statement into page text.
Bank statement OCR API
Finance extraction API
Turn bank statements into transaction-ready JSON instead of another PDF parsing problem.
Bank statements look easy until the layouts drift, scans degrade, or line-item tables spill across pages. LeapOCR helps teams extract balances, account metadata, and transaction rows into markdown or schema-fit JSON that actually fits reconciliation workflows.
Why teams use this
Extract balances, account metadata, and transaction rows from digital PDFs and low-quality scans.
Return readable markdown for review or structured JSON for reconciliation and ledger workflows.
Keep OCR inside your own finance stack instead of rebuilding cleanup logic after extraction.
Request surface
The winning workflow is usually a structured extraction contract with markdown kept available for exception review.
Statement extraction request
{ "url": "https://example.com/bank-statement.pdf", "file_name": "bank-statement.pdf", "format": "structured", "model": "standard-v1", "instructions": "Extract account metadata, opening balance, closing balance, and transaction rows.", "schema": { "type": "object", "properties": { "account_holder": { "type": "string" }, "statement_period": { "type": "string" }, "opening_balance": { "type": "number" }, "closing_balance": { "type": "number" }, "transactions": { "type": "array" } } }}
Why it works
Why bank statements break simpler tools
The challenge is not only reading the page. It is preserving transaction structure and producing a payload your finance workflow can trust.
Messy input
Scans and exports share one path
Use the same extraction surface across native bank PDFs, emailed scans, and lower-quality uploaded files.
Downstream fit
Shape the result for reconciliation
The result can be tailored to balances, running totals, posting dates, and transaction objects your downstream system actually expects.
What you control
What teams usually extract
These are the fields finance and operations teams most often need from statement workflows.
account
Header field
Account and statement metadata
Capture account holder, account number, institution, currency, and statement period without another parsing pass.
balances
Financial summary
Opening and closing balances
Pull opening, closing, and sometimes intermediate running balances into fields finance workflows can validate.
transactions
Array output
Transaction lines with dates and amounts
Return row-level objects with posting date, description, debit or credit direction, amount, and balance where present.
markdown
Readable mode
Keep a reviewable version of the page
Markdown helps reviewers trace the structured result back to the source document when exceptions happen.
Examples
Two common bank statement workflows
Most teams either need transaction-ready JSON or a readable statement view for review and exception handling.
Structured ledger flow
Return balances and transaction rows for reconciliation
This is the common path for fintech, bookkeeping, and finance-ops teams moving statement data into a ledger or rule engine.
Statement result
json
{ "account_holder": "Northwind LLC", "statement_period": "2026-02-01 to 2026-02-29", "opening_balance": 14520.33, "closing_balance": 18104.77, "transactions": [ { "posted_at": "2026-02-07", "description": "ACH CREDIT - Client Payment", "amount": 4800.0, "direction": "credit" }, { "posted_at": "2026-02-14", "description": "WIRE OUT - Vendor Settlement", "amount": 1215.56, "direction": "debit" } ]}
Review-first flow
Keep markdown for exception handling and QA
When finance teams still need to inspect the page, markdown preserves the statement in a format that is easier to review than a raw OCR blob.
Markdown excerpt
md
# Account statement- Account holder: Northwind LLC- Statement period: 2026-02-01 to 2026-02-29- Opening balance: 14520.33- Closing balance: 18104.77## Transactions| Date | Description | Debit | Credit || --- | --- | ---: | ---: || 2026-02-07 | ACH CREDIT - Client Payment | | 4800.00 || 2026-02-14 | WIRE OUT - Vendor Settlement | 1215.56 | |
FAQ
Questions teams ask before wiring this up
Straight answers for teams evaluating how this workflow fits into production.
Can LeapOCR extract transaction tables from scanned bank statements?
Yes. The workflow is designed for digital PDFs and lower-quality scans, with structured output available for transaction rows, balances, and statement metadata.
Should I use markdown or structured output for bank statements?
Use structured output when the data needs to feed reconciliation, bookkeeping, or a ledger. Use markdown when a reviewer still needs a readable statement view.
Why not use a generic PDF parser for bank statements?
Generic parsers can stop at readable text. Bank statement workflows usually need row-level transaction objects and stable financial fields that downstream systems can validate.
Ready to test
Run your ugliest bank statement through a schema-first OCR workflow
Start with a real statement, not a demo PDF. The useful test is whether the extracted rows and balances hold up in your downstream workflow.