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The n2 - n1 in the case of a negative number as a result when converted to bool will yield true. So n1 turns out to be less than n2. That's why it is a bad practice to use ints in such Boolean context.

Yes, as stated in the documentation:

...comparison function object which returns ​true if the first argument is less than the second

But the implementation of the comparison here leads to failure. Try this and see for yourself:

I had used document.getElementById('1').contentWindow.jQuery('#left').jstree(true); to get instance from iframe with id='1'. In order to listen to right iframe(with id='2') if any menu has been clicked, I used document.getElementById('2').contentWindow.jQuery('#right').on("changed.jstree",function(e,data){}). I get the instance of left iframe within this function. By using this instance, I has deselect previous selection, select current selection, and open children of selected menu.

index-12.html

I'm unsure if the problem is my code or if I found a compiler bug.

The problem is your code. In GNU assembler, parentheses are used to dereference like unary * is in C, and you can only dereference a register, not memory. As such, writing 12(%0) in the assembly when %0 might be memory is wrong. It only happens to work in GCC because GCC chooses to use a register for "rm" there, while Clang chooses to use memory. You should use "r" (bytes) instead.

Also, you need to tell the compiler that your assembly is going to modify the array, either with a memory clobber or by adding *(unsigned char (*)[16])bytes as an output. Right now, it's allowed to optimize your printf to just hardcode what the values were at the beginning of the program.

Fixed code:

Vector3 position = point.position; makes a copy of point.position. The following code then updates this copy, which in turn is thrown away when it goes out of scope at the end of the if statement.

The solution is simple enough - use a reference instead: Vector3 &position = point.position;. The rest of the code can be left as-is.

You don't want to use a placeholder image to prioritize your background images in situations like this, you want to use . That will tell the browser to start downloading the image as soon as possible.

Try adding the following code to the of your page, and then use your background image as normal. It should load much faster:

Try This :

try:

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 can use the FILTER function to remove the blanks.

Replace you lines load the arrays