language | This project was bootstrapped with Create React App | Frontend Framework library

Why do I name projects with a website in the path?

If your package has the exact same import path as someone else's package, then someone will have a hard time trying to use both packages in the same project because the import paths are not unique. So long as everyone uses a string equal to a URL that they effectively "own", such as your GitHub account (or actually own, such as your own domain), then these name collisions will not occur (excepting the fact that ownership of URLs may change over time).

It also makes it easier to go get your project, since the host location is part of the import string. Every source file that uses the package also tells you where to get it from. That is a nice property to have.

Where do I initialize git?

Your project should have some root folder that contains everything in the project, and nothing outside of the project. Initialize git in this directory. It's also common to initialize your Go module here, if it's a Go project.

You may be restricted on where to put the git root by where you're trying to host the code. For example, if hosting on GitHub, all of the code you push has to go inside a repository. This means that you can put your git root in a higher directory that contains all your repositories, but there's no way (that I know of) to actually push this to the remote. Remember that your local file system is not the same as the remote host's. You may have a local folder called github.com/myname/, but that doesn't mean that the remote end supports writing files to such a location.

I think the main misunderstanding is that my Int @a = 1,2,3 and [1,2,3] are somehow equivalent. They are not. The first case defines an array that will only take Int values. The second case defines an array that will take anything, and just happens to have Int values in it.

I'll try to cover all versions you tried, why they didn't work, and possibly how it would work. I'll be using a bare dd as proof that the body of the function was reached.

#1

Make an Array with the month names and then loop trough it accordting to initial month and end month:

Don't know about metal specifically, but in ordinary C, you'd want to put f and byteArray inside a union

Here's some sample code:

You're supposed to free memory right after the last time it will be used. In your program, after the while loop in recursion, Energy isn't used again, so you should free it right after that (i.e., right before return event_counter;).

Updated02 (Some minor tweaks needed to be made) I had to ask two questions to be able to solve it. If you are dealing with this kind of questions a lot, you are required to learn igraph package which is primarily used for network analysis. There maybe a more simple way of doing it but for now I think it will do. Let's walk you through it:

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.

Here's a solution:

I am trying to go through each of the books in holds using holds[0], holds[1] etc and test to see if the title is equal to a book title

Translated almost literally to Python: