font | OWenT 's Utils -- Font branch | User Interface library

Run ‘python manage.py collectstatic’ and try again.

The way you handle static wrong, remove the static dirs in your INSTALLED_APPS out of STATIC_DIRS and set a STATIC_ROOT then collectstatic again.

Add the following as django documentation to your urls.py

You need to make sure you include jQuery with bootstrap. You can add everything by adding this to your html:

If you are using vuetify from vue-cli-plugin-vuetify (vue add vuetify), treeshaking and auto component import is enabled by default, by using vuetify-loader.

If you look into the source code of vue-cli-plugin-vuetify, it only uses vuetify-loader if it is present in your package.json. So removing vuetify-loader from package.json should disable this behavior.

Simply add display: flex; to a in the CSS:

Try This :

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.

You need to set input image for the filter and take care of the interoperately between Image and UImage

Rather than using two different function, one for adding card image and one for card content, try combining both of them.. here use the code for your reference.

Impact may not be 'websafe' then, as it appears to NOT be installed on Androids and iOS (mobile), otherwise it would work. Unless the class "MsoNormal" has a different font-family on it. (I would remove that, it's not necessary or related to performance.)

If that fails, you'll need you to use @font-face to load it in from a public website. Keep in mind @font-face is not supported on everything: https://www.caniemail.com/features/css-at-font-face/

As a fallback, you might like to use a similar font, via Google Fonts which is already setup for this: https://fonts.google.com/specimen/Anton

But to make it work on absolutely everything, you'll need to save it as an image, and load in as