Use these examples to understand what OCRToDocs is best at, which output mode to choose, and how the result typically looks in Google Docs or Google Sheets.
Input: a scanned agreement with paragraph text, headings, and signatures.
Best output: Google Docs OCR.
Why: the goal is readable clauses, not structured rows.
Typical review points: party names, dates, section numbering, and clause breaks.
Input: a vendor invoice with header fields, totals, tax, and line items.
Best output: Google Sheets OCR.
Why: finance workflows usually need columns for vendor, invoice number, total, tax, and each item line.
Typical review points: totals, invoice number, tax amount, and merged item descriptions.
Input: a mobile photo of a receipt with merchant, date, subtotal, tax, and total.
Best output: Google Sheets OCR.
Why: expense workflows work best when receipts become a sheet of standardized fields.
Typical review points: merchant spelling, date format, subtotal versus total, and tax line detection.
Input: a table from a PDF export or screenshot with headers and several rows.
Best output: Google Sheets OCR.
Why: the value of the file is in the rows and columns.
Typical review points: header alignment, wrapped cells, blank columns, and numeric formatting.
Input: an old scanned page from a report, letter, or archive document.
Best output: Google Docs OCR.
Why: archive digitization is usually about making the text searchable, editable, and shareable.
Typical review points: faded letters, punctuation, old fonts, and paragraph breaks.
Input: a report that combines narrative text with one or more tables.
Best output: choose the result based on what matters most, or process sections separately.
Why: document text and tabular data have different extraction goals.
Typical review points: whether the table should be isolated and whether the text needs paragraph fidelity.
An OCR example is most useful when it answers three questions: what the source file looks like, which output mode is the right fit, and which fields a human should review afterward. That is why these examples focus on intent and review points, not just on raw extraction claims.
High-quality scans tend to preserve line breaks, column boundaries, and numeric values more reliably. Lower-quality images create the usual OCR failure modes: merged columns, split words, missing punctuation, and number confusion. If your file looks close to the examples above, OCRToDocs is a good fit.
The invoice PDF example is the closest match. It is designed around extracting header fields, totals, tax values, and line items into Google Sheets.
The scanned contract example is the best match. It emphasizes paragraph text and editable clauses in Google Docs.
Choose the output based on the primary goal, or split the workflow. If the table matters most, use Sheets OCR. If the paragraphs matter most, use Docs OCR.
Best for contracts, notes, reports, and archive pages.
Best for invoices, receipts, statements, and tables.
Read step-by-step guides for the most common OCR jobs.