SVG++ VS tesseract-ocr

Congratulations, now you replaced a trivial file format that (from a quick glance at the code) needed about ~35 of easily readable and self-contained Lua code to parse with an external dependency that would be much larger and harder to follow and either having (at least) an XML parser as its own dependency or implementing its own XML parsing, as well as being at the mercy of their developers. Also unless you are using some highly popular library, you may end up with some abandoned dependency.

Examples of both are at [0] (C++ based parser, you'd also need to write some bindings for lua) and [1] (Lua based parser for a subset of the format, abandoned for almost a decade).

There are times when using an external dependency might be a good idea, but a text-based file format that describes lines and can be implemented in a few lines of code is not one.

[0] https://github.com/svgpp/svgpp

[1] https://github.com/luapower/svg_parser

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.

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).

[1] https://github.com/tesseract-ocr/tesseract/issues/3447

Less that 20 years ago. Not a small project.

I have recently discovered a few very helpful github packages which help me make notes while listening to lectures. These would be 1. pix2tex (allows you to scan an equation and convert it to latex) 2. pix2text (allows you to scan an equation with words in it and converts it to latex and text) 3. Tesseract (not really a physics related package, but it does allow me to copy notes from transcripts easily) 4. Mathpix an app that performs all the above mentioned operations better than the packages above, but one which ain't free.

I suggest manually creating a dataset using scribd.com. It offers a free trial period of 30 days, but I am uncertain whether it covers unlimited documents or not. Nevertheless, there are over one million statements of purpose (SOPs) available on the site. You could also use the Scribd downloader. Some documents may be composed of a bunch of images, so you will have to use something like Tesseract OCR.

The ocr filter in ffmpeg is powered by the Tesseract library. As you will often find in ffmpeg, the build within ffmpeg has only a subset of the functionality of the original library - at least, for the moment. There's always the possibility of APIs being expanded in later ffmpeg releases. And it is open source of course, so there's the option of instigating those changes yourself - or using the original library in conjunction with ffmpeg if that suits your needs better.

After that you would use Tesseract-OCR to OCR the pages. Tesseract is a open source multiplatform OCR software. If the typeface is something non standard you would have to train the recognition engine on your data.

Decent OCR: https://github.com/tesseract-ocr/tesseract

tesseract-ocr/tesseract: Tesseract Open Source OCR Engine (main repository)