Drop in any PDF — bank statement, invoice, table, financial report, or scanned document — and get a clean CSV with the columns you need: structured rows, proper data types (dates as dates, numbers as numbers), wrapped text rejoined, multi-page tables stitched.
Generic PDF-to-CSV converters do one of two things: extract text positions and dump them as rows (producing CSVs where columns drift across rows whenever a vendor name wraps to two lines) or fragment tables across multiple files (one CSV per page even when the table continues). Either way, you end up with output that needs an hour of cleanup before it's usable in Excel, Python, or your data warehouse.
Specialty tools that actually produce clean CSVs charge enterprise prices, demand template setup per document type, or limit you to a handful of supported formats. Bookkeepers, analysts, and anyone working with PDFs in 2026 still pay too much for too little — or accept the cleanup tax.
PDFExcel reads PDFs by structure. Each row in the source becomes a row in the CSV. Wrapped text gets rejoined into single cells. Multi-page tables stitch automatically. Dates emit as dates, numbers as numbers, currency as numeric values (with a separate Currency column when the source has multiple). Negative amounts stay negative. Headers get cleaned and deduplicated against the data.
Same workflow whether you're converting a bank statement, invoice, or financial report. Same workflow whether the source is a native PDF or a scanned image PDF. Drop the CSV into Excel, Google Sheets, Python (pandas), R, Power BI, Tableau, or load directly into a SQL warehouse via your ETL tool.
The model knows the difference between a wrapped vendor name (one cell) and a sub-row in a hierarchical table (separate row). Trained on real document tables — not just generic 2D grids.
Most PDF-to-CSV tools either need template setup or produce CSV that needs cleanup. PDFExcel produces clean, structured CSV first try — useful for ETL workflows, ad-hoc analysis, and anything that's downstream of a PDF source.
Structured rows, proper data types, multi-page tables stitched. UTF-8 encoded, ready for Excel, pandas, or your data warehouse.
| # | Date | Description | Debit | Credit | Balance |
|---|---|---|---|---|---|
| 1 | 2025-03-02 | Opening Balance | 24318.42 | ||
| 2 | 2025-03-03 | ACH CREDIT - STRIPE PAYMENTS | 4210.00 | 28528.42 | |
| 3 | 2025-03-05 | CHECK #1432 - Smithson HVAC | 1875.00 | 26653.42 | |
| 4 | 2025-03-08 | ZELLE TO Acme Supply | 612.50 | 26040.92 | |
| 5 | 2025-03-11 | WIRE TRANSFER IN - Acme Capital | 15000.00 | 41040.92 |
Data analysts loading PDF tables into pandas / R / Power BI, developers building ETL pipelines that ingest PDFs, bookkeepers exporting to systems that prefer CSV over Excel, finance teams pushing PDF data into data warehouses.
Pulls 100+ company financial statements from SEC EDGAR for a sector analysis. Convert each to CSV in batch, load into pandas for sector-level pivot. Hours of manual entry replaced with a 5-minute upload.
Ingests partner bank statements weekly into a data warehouse. PDFExcel produces clean CSV that loads directly via the warehouse's COPY command — no Python pre-processing step needed.
Client uses a custom accounting system that imports CSV. Convert monthly statements to CSV with the system's expected column names, import — done.
Generic tools extract text by position and dump it as CSV rows — producing output where columns drift, wrapped text fragments, and multi-page tables split across files. PDFExcel reads tables by structure, so wrapped vendor names rejoin, multi-page tables stitch, and data types (date, number, currency) emit correctly the first time.
Yes. Dates emit in your specified format (YYYY-MM-DD by default for CSV; MM/DD/YYYY for Excel). Numbers emit as raw numeric values without currency symbols (a separate Currency column tracks $ / € / £). Negatives stay negative.
UTF-8 by default, with an optional BOM for Excel-on-Windows compatibility (some Excel versions need the BOM to detect encoding correctly). On request, we also export tab-delimited or pipe-delimited.
Yes. Built-in OCR runs automatically when there's no embedded text layer. Same workflow, same clean CSV at the end.
10 documents per month, free, forever. Plans from $69/month for 50 documents. Most ad-hoc analysis or small ETL workflows fit Starter or Pro comfortably.