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How to Create a Custom Mac App for Precision AI-Powered OCR

Build a focused Mac utility for selecting one article, signature, or passage inside a difficult historical scan. OCR Party crops the exact region, offers local or AI OCR, returns editable text, and records where the crop came from.

How to Create a Custom Mac App for Precision AI-Powered OCR

Source episode · 32:20

How I AI: Tim McAleer's AI Workflows for Documentary Filmmaking at Florentine Films

with Claire Vo

Tim opens a damaged newspaper in OCR Party, draws a box around one article, switches between macOS Vision and AI OCR, and receives text plus the crop location for editors.

Read the full episodeWatch on YouTubeListen on SpotifyListen on Apple

Before you start

What you need

  • A Mac with Xcode
  • Historical document images
  • Apple Vision and optional AI OCR access
  • A place to store transcript and crop metadata

What you’ll make

A Mac menu bar app that extracts text from a selected image region and preserves its location on the source document.

Step by step

The workflow

Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.

5 steps

Step01

Design the User Interface

Define a minimal document workflow: open an image, zoom and pan, draw one crop, choose the OCR engine, inspect the text, copy it, and save the crop reference. Keep the app focused on partial document OCR.

Example prompt
Write a PRD for a Mac menu bar utility called OCR Party. A historical researcher opens a document image, zooms and pans, draws a precise rectangular crop, chooses Apple Vision or an AI OCR service, reviews editable text beside the crop, copies it, and saves original pixel coordinates. Include uncertainty and error states.

For a utility like this, keeping the interface minimal and focused on the core task is key. You can use an LLM to help brainstorm the layout or generate boilerplate code for the app.

Step02

Implement a Region Selection Tool

Build an image viewer with zoom, pan, and a resizable selection overlay. Convert the visible rectangle to original image pixels so the same crop can be reproduced regardless of window size or zoom.

Step03

Integrate an AI OCR API

Send only the cropped pixels to the selected OCR engine. For AI OCR, ask for a faithful transcription that preserves line breaks and marks unreadable text instead of completing it silently.

Example prompt
Transcribe this cropped historical document exactly. Preserve line breaks, spelling, capitalization, and punctuation as shown. Use `[unclear]` for unreadable text and do not invent missing words. Return only the transcription.
Step04

Display and Copy the Results

Show the crop and editable transcript together. Add copy, save, and retry controls so a researcher can correct uncertain text without repeating the selection.

Step05

Store Crop Coordinates (Optional)

Save the source filename, original pixel rectangle, OCR engine, timestamp, and corrected text with the transcript. Use those values to reopen or highlight the same location later.

What good looks like

  • Users can zoom and draw a precise crop around the target text.
  • The app returns editable text from damaged print or handwriting.
  • The user can choose local or API based OCR.
  • Saved coordinates locate the excerpt on the original image.

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After the steps

Runbook notes

How to recover when the loop fails and where human judgment helps.

Recover

If it goes sideways

The selection includes neighboring columns or misses text
Add zoom, pan, resizable handles, and a crop preview before OCR. Preserve the source image coordinates after display scaling.
AI fills damaged text too confidently
Mark low confidence words or uncertain spans and show the selected image beside the transcript for correction.
Rotated or skewed text transcribes poorly
Offer deskew and rotation controls before sending the crop, then retry the same region.
Saved crop coordinates do not map back to the original
Convert the selection from view coordinates into original pixel coordinates and test at multiple zoom levels.

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