ITK VS tesseract-ocr

The itkImage.h header is ITK's standard n-dimensional image data structure.

In this post, we review how the Insight Toolkit (ITK) leverages the first-interaction GitHub Action to communicate our appreciation of the efforts of first-time contributors, establish norms for behavior, and provide civil pointers on where to find more information.

Jupyter has emerged as a fundamental component in artificial intelligence (AI) solution development and scientific inquiry. Jupyter notebooks are prevelant in modern education, commercial applications, and academic research. The Insight Toolkit (ITK) is an open source, cross-platform toolkit for N-dimensional processing, segmentation, and registration used to obtain quantitative insights from medical, biomicroscopy, material science, and geoscience images. The ITK community highly values scientific reproducibility and software sustainability. As a result, advanced computational methods in the toolkit have a dramatically larger impact because they can be reproducibly applied in derived research or commercial applications.

It also depends heavily on the toolchain. One of the first successful toolkits used to circumvent image-based security measures was ITK, originally a toolkit for medical image processing. That's not even using AI (at least back then). Here you build "piplines" by lego'ing together functions like building blocks, there are rules to it, but the sleek interface design make it very versatile. It was a nightmare to devise ways to counteract, since the crucial processing steps could easily be switched around as long as the linear algebra made sense. And when you have a toolchain excelling in fourier-space based analysis and interaction, the linear algebra makes sense in a lot of different orders of doing steps.

Yeah, Prolog is pretty cool!

Another technology I found interesting too learn is ITK (https://itk.org/). You need a different mindset using ITK than other image processing libraries.

Lisp is cool as well.

And fully homomorphic encryption.

VTK is a powerful visualization package, but it's more for working with 3D FEM/CFD data. There are lots of things in there though, so it may be useful. The sister project Paraview is an application which can be used to work with data interactively. Both have great Python support. There is also ITK which is focused on with image data, like medical scans - never used it, though.

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).