o | Language for code-golf with a focus on unreadability | Interpreter library

The problem is that your CSS selectors include parentheses () and dollar signs $. These symbols already have a special meaning. See:

• () - Are parentheses allowed in CSS selectors?
• $ - [attribute$=value] Selector

You can escape these characters using a backslash \.

The short answer is, you can't. Automatic variables, as made clear in the documentation, are only set inside the recipe of a rule, not when expanding the prerequisites. There are advanced features you can take advantage of to work around this, but they are intended only to be used in very complicated situations which this isn't, really.

What you want to do is exactly what pattern rules were created to support:

first of all, you can check count of std::vector to see if given key exists

Here is how it works:

Tesseract takes a lang variable that you can expand to include different languages if they're installed. I've used the UB Mannheim (https://github.com/UB-Mannheim/tesseract/wiki) installation which includes a ton of languages supported.

To get better and more accurate results, the best thing to do is to process the image before handing it to Tesseract. Set a white/black threshold so that you have black text on white background with no shading. I'm not sure how to do this in Node, but I've done it with Python's OpenCV library.

If that font doesn't get you decent results with the out of the box, then you'll want to train your own, yes. This blog post walks through the process in great detail: https://towardsdatascience.com/simple-ocr-with-tesseract-a4341e4564b6. It revolves around using the jTessBoxEditor to hand-label the objects detected in the images you're using.

Edit: In brief, the process to train your own:

1. Install jTessBoxEditor (https://sourceforge.net/projects/vietocr/files/jTessBoxEditor/). Requires Java Runtime installed as well.
2. Collect your training images. They want to be .tiffs. I found I got fairly accurate results with not a whole lot of images that had a good sample of all the characters I wanted to detect. Maybe 30/40 images. It's tedious, so you don't want to do TOO many, but need enough in order to get a good sampling.
3. Use jTessBoxEditor to merge all the images into a single .tiff
4. Create a training label file (.box)j. This is done with Tesseract itself. tesseract your_language.font.exp0.tif your_language.font.exp0 makebox
5. Now you can open the box file in jTessBoxEditor and you'll see how/where it detected the characters. Bounding boxes and what character it saw. The tedious part: Hand fix all the bounding boxes and characters to accurately represent what is in the images. Not joking, it's tedious. Slap some tv episodes up and just churn through it.
6. Train the tesseract model itself

• save a file: font_properties who's content is font 0 0 0 0 0
• run the following commands:

tesseract num.font.exp0.tif font_name.font.exp0 nobatch box.train

unicharset_extractor font_name.font.exp0.box

shapeclustering -F font_properties -U unicharset -O font_name.unicharset font_name.font.exp0.tr

mftraining -F font_properties -U unicharset -O font_name.unicharset font_name.font.exp0.tr

cntraining font_name.font.exp0.tr

You should, in there close to the end see some output that looks like this:

Master shape_table:Number of shapes = 10 max unichars = 1 number with multiple unichars = 0

That number of shapes should roughly be the number of characters present in all the image files you've provided.

If it went well, you should have 4 files created: inttemp normproto pffmtable shapetable. Rename them all with the prefix of your_language from before. So e.g. your_language.inttemp etc.

Then run:

combine_tessdata your_language

The file: your_language.traineddata is the model. Copy that into your Tesseract's data folder. On Windows, it'll be like: C:\Program Files x86\tesseract\4.0\tessdata and on Linux it's probably something like /usr/shared/tesseract/4.0/tessdata.

Then when you run Tesseract, you'll pass the lang=your_language. I found best results when I still passed an existing language as well, so like for my stuff it was still English I was grabbing, just funny fonts. So I still wanted the English as well, so I'd pass: lang=your_language+eng.

To get the content as shown in your example TEXT.txt, you could just use a For /F loop and use the = character as the delimiters:

You are using cmake. The makefiles generated by cmake don't conform to "standard" makefile conventions; in particular they don't use the CXXFLAGS variable.

When you're using cmake, you're not expected to modify the compiler options by changing the invocation of make. Instead, you're expected to modify the compiler options by either editing the CMakeLists.txt file, or else by providing an overridden value to the cmake command line that is used to generate your makefiles.

You should separate creating your menu from your content. Please review the following example. I decoupled your menu, component, and event logic into meaningful phases.

An option with tidyverse

1. Get the objects in a named list (dplyr::lst)
2. Convert the named list to a tibble - enframe
3. unnest the list column
4. Extract the substring from the 'name', convert it to upper case
5. Do a join (right_join) with the 'chars' converted to a tibble
6. arrange the rows after replacing the NA with 'Unique'
7. pull the column as a vector