An upload control can look simple while hiding a product decision: what does the person want to accomplish with the file? A scanned page, a photo of a document, searchable text, and a spreadsheet-ready receipt are related, but they are not the same job. A useful interface makes those paths legible before the user starts.
ScanPDF AI presents four entry points—Scan PDF, Photo to PDF, OCR PDF, and Receipt to Excel. The interface gives us a concrete example of organizing around outcomes rather than asking everyone to start with one generic “upload” action.
A task selector is part of the product model
Designing workflow labels means deciding which distinctions matter to users. For document work, the source file may be a PDF, a phone image, or a photo of a receipt. Yet the source alone does not tell us the desired result. Someone may want a portable document, extracted text, or structured transaction details.
Putting those options near the start lets people choose by intent. It also helps a team keep each workflow’s promise narrow. A photo-to-PDF path can focus on document conversion. An OCR path can explain recognized-text exports. A receipt path can set expectations about fields and spreadsheet output.
Show an output contract before upload
The OCR page lists PDF, JPG, PNG, and WebP inputs and describes PDF, TXT, and DOCX exports. The receipt page describes PDF, TXT, and CSV output, and names merchant, date, total, tax, currency, and line items. This is a useful pattern: tell users what kind of artifact a workflow is intended to produce before they invest time in it.
For product designers, the next improvement to look for is whether that contract remains visible after selection. Are supported formats near the file picker? Is the output type clear before a potentially lengthy upload? Does the result screen distinguish editable text from a formatted document? These are practical review questions, not assumptions about any one implementation.
Keep “recognized” separate from “verified”
OCR and extraction outputs can look authoritative because they are neatly formatted. That presentation should not imply perfect recognition. A receipt total might be confused with a subtotal; a date can be ambiguous; a low-quality image can omit a line. Products should make it easy to inspect extracted values and correct them before export.
An example on a marketing page can explain the shape of the result, but it is not evidence of performance on arbitrary files. Teams evaluating a tool should test varied samples, including skewed photos, faded print, multiple currencies, and receipts with several tax or line-item formats. Review actual outputs before incorporating them into an operational workflow.
Design for the whole handoff
The interaction does not end when a file is accepted. People need to know how the document will be processed, where the result appears, which export fits their next tool, and what they should verify. Good labels and clear output options can remove uncertainty without adding more controls.
That is the broader lesson from these four paths: map the interface to the user’s next decision. When scanning, conversion, OCR, and structured extraction are presented as distinct jobs, the product can explain each one honestly—and users can pick the outcome they actually need.