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This doesn't quite do what you asked, but this is probably what you are looking for. It replaces the H:\ in a filename with file://server/certs/ and reverses the \ to / anywhere else. This makes the assumption that these are simple windows drive letter replacements attachment names, so H:\ can't really appear anywhere else other than at the beginning.

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.

The optional identifier ? is for the last thing, either a characters or group. So the expression ([a-z0-9]{1,3})_? makes the underscore optional, but not the preceding group. The solution is to move the underscore into the parenthesis.

The auto-population of 0 in empty cells was solved by simply opening excel-> Click on file-> Options -> Advanced -> de-select the checkbox containing "Show a zero in cells that have zero value. And for formula, you need to make sure that all cells involved in, should be of same format. Click on cell then right click, then select format and cross check the if format are same.

It varies between implementations, but generally, this is managed by the cache coherency protocol of the multiprocessor. In simplest terms, what happens is that when CPU1 writes to a memory location, that location will be invalidated in every other cache in the system. So that write will invalidate the line in CPU2's instruction cache as well as any (partially) decoded instructions in CPU2's uop cache (if it has such a thing). So when CPU2 goes to fetch/execute the next instruction, all those caches will miss and it will stall while things are refetched. Depending on the cache coherency protocol, that may involve waiting for the write to get to memory, or may fetch the modified data directly from CPU1's dcache, or things might go via some shared cache.

IDs MUST be unique - instead use class

and why the interval?

I recommend you to have a read to ContentResolver documentation and then read this Retriefe-info documentation then you'll be able to get the extension of your file.

I finally found the answer to this in an issue on github which addresses this exact problem:

Is your feature request related to a problem? Please describe. It is an improvement. At the moment, if you use 'PDFDownloadLink' the PDF is being generated as the component loads.

Describe the solution you'd like It is not mandatory, but having multiple heavy PDFs ready to be downloaded wouldn't be the best approach since not every user will need it.

Describe alternatives you've considered I've used pdf() function to generate the blob and file-saver lib to download it: