OCR tools such as ABBYY FineReader PDF, Adobe Acrobat (Scan & OCR), Readiris 17, and OmniPage are widely used to convert scanned pages into editable text. They handle the heavy lifting of recognition, but their output almost always contains familiar OCR artefacts: split words at line breaks, strange spacing, and character substitutions like “l” for “1” or “rn” for “m.”
AI tools, like Perplexity Pro, ChatGPT and Claude, can step in as a targeted post‑processing layer. Instead of the time-consuming process of checking each suspicious character in your OCR tool or word processor, you let the AI work at sentence and paragraph level, using context to decide what the text was meant to say while keeping the content intact.
Suggested workflow
Run OCR and export text
Use your preferred OCR tool to recognize the document and export the result as plain text, Markdown, or Word. Avoid exporting to formats that hide extra formatting (e.g. heavily styled PDFs), because you want clean, editable text for the AI.Send chunks to the AI with a focused prompt
Work in sections (for example, 5–10 pages at a time). Prompt example:
“This text comes from OCR and contains typical OCR artefacts. Correct broken words, spacing, punctuation, and capitalization, but preserve all content, line breaks, and headings as much as possible. Do not summarize or omit anything.”Review against the scan in a side‑by‑side view
Open the scanned PDF/image on one side of your screen and the AI‑corrected text on the other. Check critical areas: headings, numbers, tables, and domain‑specific terms (e.g. drug names, legal references, codes). Correct any terminology the AI “normalized” incorrectly.Use a diff tool between raw OCR and AI output
Run a text diff between the raw OCR file and the AI‑corrected version. This helps you see exactly what changed, confirm that nothing was dropped, and quickly scan for any over‑confident “corrections” you don’t want.Automate for larger volumes
For recurring projects, you can script the process: batch‑export from your OCR tool, segment the text, send each chunk to the AI via API, then reassemble the cleaned text. Human effort can then be reserved for spot‑checking and final QA instead of first‑pass cleanup.
By combining a robust OCR engine with an AI cleanup step, you move from “barely readable extraction” to text that is reliable enough for translation, further editing, or long‑term archiving, without spending hours fixing the same types of errors by hand.