# Create directory at TargetFolderPSX with command 'mkdir -p ' and tag -p, so that no error will result if directory already exists and allows making parent directories if needed. Set TargetFolderPSX to TempFolderPSX & 'Tesseract/' The escape key will cancel the interactive screen shot.ĭo shell script 'open ' & ScreenCaptureFilePSX

The space key will toggle between mouse selection and window selection modes. The control key will cause the screen shot to go to the clipboard. # option -i Capture screen interactively, by selection or window. # screencapture similar to key command shift 4.

Set screencaptureSh to '/usr/sbin/screencapture' & ' -i ' & ScreenCaptureFilePSX Set ScreenCaptureFilePSX to TempFolderPSX & TimeStamp & '.png' Set TimeStamp to (current date) as «class isot» as string Set TempFolderPSX to POSIX path of (path to temporary items folder)

So your best bet would be to Google 'CoreML Text Recognition Example', as I seem to remember coming across many such examples during my own CoreML learning period. I could be wrong, but I don't even think you can use ObjectiveC either.Īnd AppleScript could only work with the Python based ML libraries, through a Python command line tool, via AppleScript's 'do shell script command', so this would require you to learn both Python and AppleScript. So you've landed in the wrong forum for character or word recognition software capabilities.ĪppleScript cannot work with Apple's Vision or CoreML frameworks, as they require Swift code. I've not used the Vision Framework, as my projects where dealing with large numerical datasets, but my understanding is that it's popular for image recognition, and could also possibly be used for text recognition. The way OCR capabilities are done these days, is with AI or Machine Learning frameworks and libraries, the popular ones are Google's TensorFlow, or scikit-learn, better known as SKLearn, or even Keras, but there are many more, most of them require you to almost exclusively use Python.Īlthough I've used SKLearn myself for a numerical data analytical Swift coded app, I did a couple of years ago, but I had to create a Python command line tool to talk to the SKLearn library, and then access the command line tool from the Swift code, so not an ideal solution.Īpple's CoreML framework has matured a lot from it's original first release, which required you to load already trained models created with the Python based ML libraries, so I've since replaced the SKLearn library with using CoreML, although I'm using numerical regressor models created with the CreateML classes, and not text recognition models, although CoreML does have the features for text recognition, and you can at least now create your own models with CoreML, rather than having to load already trained models from elsewhere.

Tesseract ocr for mac os x