book | 文言陰符 An Introduction to Programming in Wenyan Language | Learning library

The difference in behaviour can be accounted for by this behaviour, described in (for instance) the following note in ECMAScript 2022 Language Specification sect 14.3.2.1:

NOTE: If a VariableDeclaration is nested within a with statement and the BindingIdentifier in the VariableDeclaration is the same as a property name of the binding object of the with statement's object Environment Record, then step 5 will assign value to the property instead of assigning to the VariableEnvironment binding of the Identifier.

In the first case:

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.

I don't see what the id column is needed for. So see if you can work with this:

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:

This works:

• c, which is used in c != '\n', is not initialized at first. Its initial value is indeterminate and using is value without initializng invokes undefined behavior.
• You are checking line[i] != '\0', but you never assigned '\0' to line unless '\0' is read from the stream.
• You should initialize i before the second loop and update i during the second loop.
• Return values of getchar() should be assigned to int to distinguish between EOF and an valid character.
• You should perform index check not to cause buffer overrun.

Fixed code:

First of all, did you consider to store language in your database? I mean language are mostly the same, doesn't change too often, you can also store in a properties file and read them at runtime to use them later.

Anyway, I think you should:

• first get from external system languages
• store in variable / in memory cache ( like a Map where you can store id and name )
• read your data from database
  • for each row you do
    • read book language id, read the cache, get out data you need

If you can't change model, just use a dto with your entity and the language and you're fine

You can concatenate your individual lists into a single Numpy array and then just use min and max along the desired axis:

This is not consumed by json_normalize directly (after my tries). Since the number of BUY and SELL are different, and these record do not neccessarily should match each other (located on a same row), suggestions is to split into two dataframes and then concatenate.