Try document conversion tools with AI

Two PDFs can need different tools. One may contain selectable text; the other may be a stack of scanned page images. Try selecting a sentence first. If it works, local parsing may be enough for Markdown. If it does not, the page needs OCR. For a screenshot or phone photo, check focus, glare, perspective and cropped edges before upload. Missing pixels cannot be recovered later.

Now look at the content. Use Markdown for prose, Excel for tables that need formulas or typed cells, CSV for one flat import table, and JSON when software needs fields, rows and metadata. Invoices, receipts and bank statements deserve their own tools because the review checks are different. An invoice number, a handwritten tip and a running balance do not fail in the same way.

A spotless sample tells you very little about the supplier scans that arrive late on Friday. Upload a representative file with the page count, dense tables and layout changes you actually receive. Before you start, note a few controls: page count, expected rows and any printed total. For an invoice, record the supplier, full invoice number and grand total. For a bank statement, record the period and both balances.

Compare the result with the source. Start with the fields that could change a payment or break an import, then inspect page breaks and row boundaries. Recalculate totals. If glare, a fold or a crop hides a value, get a better image. Do not guess. These tools prepare a draft for review; they do not approve transactions, reconcile accounts or decide whether a document is legally valid.

Excel keeps multiple tables, dates and numeric cells. CSV is easier to import but cannot store sheets or cell types. JSON carries fields, rows and metadata, although your application still needs a versioned schema. Markdown keeps the words editable; it does not rebuild the page design.

Open the export in the program that will consume it. Check leading zeroes, ambiguous dates, currency, blank cells and decimal separators again after import. A valid CSV can still fail in a locale-specific importer. Valid JSON can still contain the wrong value. The handoff is finished when the receiving system reads the reviewed data correctly.

OCR reads pixels. It cannot restore a cropped column or a total hidden by glare. Worse, some errors look believable: 0 becomes O, 1 becomes I, or a decimal point disappears. A table can contain every visible word and still put one amount in the wrong row. Confidence scores help you decide where to look first. They do not replace source review.

Fix the input before swapping models. Photograph the page square on, use even light and keep every edge visible. Prefer the original file to a compressed chat attachment. On multi-page tables, inspect the last row of one page and the first row of the next. When several layouts recur, test the hardest example from each one before you automate the folder.

One-file tools prove the input and output. A recurring document extraction workflow also needs an owner for low-confidence fields, duplicates, missing pages, arithmetic differences and schema failures. Decide which values always need a person. Keep the source, accepted result, corrections and export together. A supplier can change a layout overnight, so test saved mappings against new versions instead of assuming the old rows still line up.

Check data handling before upload. Local PDF to Markdown reads a text-layer PDF in the browser and does not send the document to an AI provider. OCR and structured extraction may send content to the provider selected for the run. Anonymous originals stay in browser storage instead of cloud file storage. For confidential files, review the provider, privacy and retention settings first. Move to stored files, batch jobs, connected exports or the API only when the process needs them.