Amazon Textract is an AWS API that returns JSON blocks to software you write. If what you actually need is a bank statement in columns, upload the PDF here and download the Excel file in a couple of minutes, with no AWS account and no code.
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Textract is excellent OCR infrastructure. Buyers go looking for something else once they realize they were shopping for a finished spreadsheet and what AWS sells is a raw extraction service that someone still has to build on top of.
Textract returns a graph of BLOCK objects with geometry, confidence scores, and cell relationships. Turning that into a columned XLSX with correct dates, signed amounts, and a running balance is an application you write, not a download button.
A project starts with an AWS account, IAM permissions, a region choice, an S3 bucket for anything multi-page, and an SDK call. That is an afternoon of setup before you see a single transaction, and it never gets shorter for the next user.
Textract finds tables on a page. A bank statement is not one table: it is a sequence of sections that continue across pages, with an opening and closing balance that has to foot. Stitching page-level tables back into one ordered ledger is the part nobody budgets for.
Analyze Document with the Tables feature is $15 per 1,000 pages and Forms is $50 per 1,000 pages. Analyze Lending, the feature actually aimed at loan packages, is $70 per 1,000 pages. A single monthly statement is often five to twelve pages.
The three month free tier gives 1,000 pages a month of plain text detection, but only 100 pages a month of Analyze Document with Forms, Tables, or Layout. For statements, 100 pages is roughly one account-year. Then it ends entirely after three months.
Anything longer than a single-page image goes through StartDocumentAnalysis, an S3 object, and either polling or an SNS notification. That is the right design for scale and completely disproportionate if a bookkeeper just has forty PDFs to get through.
BankXLSX is roughly the application most teams end up building on top of a raw OCR API, except it is already finished and limited on purpose to bank and card statements.
Download an XLSX or CSV with date, description, amount, and running balance already in columns, in transaction order.
No AWS account, no IAM role, no S3 bucket, no SDK. Open the page, upload the statement, take the file.
A twelve page statement, or twelve months in one PDF, comes out as a single continuous ledger rather than a dozen disconnected tables.
Scanned statements and phone photos are read out of the box, in PDF, JPG, PNG, BMP, HEIC, and TIFF.
Tuned to the layouts, date formats, and balance columns that Chase, Bank of America, Wells Fargo, Citi, Capital One, and dozens more actually print.
Export QBO, OFX, QFX, and QIF alongside Excel and CSV, so the statement imports straight into QuickBooks, Xero, or Quicken.
No cloud console, no API key, no credit card.
Drop in a PDF or scanned bank statement. Multi-page and multi-month files are fine.
Tip: Password-protected PDFs are supported.
Every transaction is pulled into structured columns automatically, balance included.
Tip: Scans are read with built-in OCR.
Take the Excel or CSV, or export an accounting format for your ledger.
Tip: Columns come out reconciliation-ready.
An honest side-by-side, including the cases where Textract is clearly the right choice.
Convert client statement PDFs without an engineer or a cloud account.
Build tax and audit workpapers from multi-month statements in minutes.
Get statement data into Excel for reconciliation and month-end close.
Spread borrower transaction history without building a pipeline first.
Textract is a component and BankXLSX is a product. That single distinction explains almost every difference below. Textract gives your software the ability to read a document; it does not decide what a bank statement is, what order the rows go in, or whether the closing balance foots. If an engineer is going to consume that output inside an application you own, Textract is a reasonable foundation. If a person is going to open the result in Excel, you are being sold a foundation when you wanted a building.
| What you are comparing | BankXLSX | Amazon Textract |
|---|---|---|
| Product type | Finished browser tool for statements | Cloud OCR and document analysis API |
| Who uses it | Bookkeepers, accountants, business owners | Developers building on AWS |
| Setup required | None, upload and download | AWS account, IAM, S3, SDK integration |
| Output | XLSX and CSV, plus QBO, OFX, QFX, QIF | JSON blocks with geometry and confidence |
| Published price | Self-serve plans, free to try | Tables $15, Forms $50, Analyze Lending $70 per 1,000 pages |
| Free allowance | Free to try, no credit card | 100 pages a month of Tables or Forms, first 3 months only |
| Multi-page handling | One continuous ledger across pages | Asynchronous job via S3, results per page |
| Running balance | Preserved as its own column | Whatever your code reconstructs |
| Data residency control | Processed by the service, deletable on demand | Stays inside your own AWS account and region |
| Best for | Firms and businesses converting statements as a task | Engineering teams embedding extraction at scale |
AWS prices taken from the Amazon Textract pricing page (US West Oregon, first one million pages) in July 2026. Rates vary by region and vendors change plans, so confirm current figures before you buy.
Three things, and they are real. It scales without you thinking about it, so a lender processing fifty thousand borrower documents a month pays per page and never provisions a server. It keeps the documents inside your own AWS account and region, which matters to compliance teams who do not want financial PDFs leaving their boundary. And Analyze Lending is a genuinely purpose-built feature: AWS built it to classify and split mortgage loan packages, bank statements included, which is more statement awareness than most general OCR services offer.
If you are an engineering team with continuous volume and an existing AWS footprint, that combination is hard to argue with. The per-page rate is low, the reliability is somebody else's problem, and you keep full control of the workflow. Nothing on this page suggests otherwise.
The line item is the cheap part. The expensive part is everything between the JSON response and a spreadsheet an accountant will sign off on. You need code that identifies which detected table is the transaction register and which is the fee summary. You need logic that carries a transaction across a page break. You need sign handling, because banks print debits in a separate column on some layouts and as a negative number on others. You need date parsing that survives a statement period spanning a year boundary. And you need a check that the closing balance foots, because an extraction that drops one row out of four hundred is not 99.75 percent correct, it is wrong in a way that is expensive to find later.
Teams routinely estimate one sprint for that work and discover it is a small product with an ongoing maintenance cost, because every new bank layout is a new edge case. That is the calculation worth making honestly before choosing between an API and a finished converter.
Statements are longer than people remember. A business checking account with a few hundred transactions a month commonly prints eight to twelve pages. One account-year is therefore roughly 60 to 140 pages. Reconstructing three years of history for a loan file or an audit can pass a thousand pages for a single client. Run that number against $15 per 1,000 pages for Tables, or $70 per 1,000 for Analyze Lending, before you compare it against a flat subscription, and compare it against a converter that charges by statement rather than by page.
Pick Textract when extraction has to happen without a person present, at volume, inside software you own, and your compliance posture requires the documents to stay in your own cloud account. Pick a converter when the deliverable is a file, the work is bounded, and the person doing it is an accountant rather than an engineer. Volume and who operates it decide this, not accuracy.
If you want the API convenience without building the statement layer yourself, the bank statement converter API returns statement-shaped output directly. If the spreadsheet is the deliverable, the bank statement converter on the homepage is free to try on a real file, and once the data is in a sheet you can categorize the transactions or run a bank reconciliation.
Yes, Textract reads the text and detects tables on a bank statement, and the Analyze Lending feature is built specifically for loan packages that include statements. It returns JSON, not a spreadsheet, so producing a usable ledger still means writing code that orders rows, handles page breaks, and checks the balance.
On the published US West Oregon rates for the first million pages a month, plain text detection is $1.50 per 1,000 pages, Analyze Document with Tables or Queries is $15, Forms is $50, Analyze Expense is $10, and Analyze Lending is $70. Prices checked in July 2026.
No. Textract returns JSON block objects describing text, tables, cells, geometry, and confidence scores. Getting an XLSX file out of it requires an application that maps those blocks into columns, which is the step a finished converter has already done for you.
Yes, for the first three months on a new AWS account. It covers 1,000 pages a month of Detect Document Text but only 100 pages a month of Analyze Document with Forms, Tables, or Layout, which is about one year of statements for a single account.
Textract is an OCR service that gives software the ability to read documents. A bank statement converter is a finished application that knows what a statement is, reconstructs it across pages, preserves the running balance, and hands back a spreadsheet a person can use immediately.
Analyze Lending is an AWS feature that classifies and splits mortgage loan packages into document types, including bank statements, pay stubs, and tax forms, then extracts data from each. It is priced at $70 per 1,000 pages and is still an API that returns structured JSON.
No. A browser-based converter does the same job with no cloud account, no IAM role, and no code. AWS only makes sense when extraction has to run automatically inside software you operate, at volume, without a person involved.
Accuracy depends on tuning, not on category. A general OCR service reads characters very well but does not know a statement must foot. Software built for statements validates row order, signs, and the closing balance, which is what actually catches an incomplete extraction.
The other big cloud OCR platform, compared.
A developer OCR API, compared side by side.
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| Pro AI Best accuracy | 2,000 pages |
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