tesseract-ocr VS OpenCV

You will also need to install the Tesseract OCR engine, which can be downloaded and installed from the following link: https://github.com/tesseract-ocr/tesseract

PyTesseract Module [ Github ] EasyOCR Module [ Github ] PaddlePaddle OCR [ Github ]

It uses teseract for the OCR https://github.com/tesseract-ocr/tesseract

Last update was pretty recent, and the git mentions tesseract 5 as a dep. so it's likely moved on a bit from when you last tried it:

https://github.com/tesseract-ocr/tesseract/releases

I suppose it depends on your use-case. For personal tasks like this it should be more than sufficient, and won't need user details/cc or whatever to use it.

Tesseract is an open-source OCR engine developed by Google. It is highly accurate and supports multiple languages. This library will do all the heavy lifting for us. We'll use it in this tutorial to quickly read the text in some images.

> Does android even have native OCR?

Tesseract? https://github.com/tesseract-ocr/tesseract

Install Google Tesseract OCR (additional info how to install the engine on Linux, Mac OSX and Windows). You must be able to invoke the tesseract command as tesseract. If this isn’t the case, for example because tesseract isn’t in your PATH, you will have to change the “tesseract_cmd” variable pytesseract.pytesseract.tesseract_cmd. Under Debian/Ubuntu you can use the package tesseract-ocr. For Mac OS users. please install homebrew package tesseract.

OCR detection will be done with Tesseract.

I’ve used Tesseract for this. It seems to work well with tabular data. https://github.com/tesseract-ocr/tesseract

If you go this route, then using an app that can convert your handwritten notes to a digital format (indexed text), will give you a good balance between cognitive processing and efficient data storage/management; you can likely find many such apps on the App Store or Google Play. If you're interested in something more hands-on, on Arch you can probably experiment with Tesseract OCR in an interesting way (Example).

Referenceshttps https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-image-dataset-balanced?fbclid=IwAR3wbTp8l5yo_5fx6HAX8Vd2-9cca3khAc8EiBGFObaALfdVid29IuB_rYE https://keras.io/api/applications/vgg/ https://www.tensorflow.org/tutorials/images/cnn?hl=th https://opencv.org/

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)

You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).

The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.