RapidFuzz VS thefuzz

Tried it, but it fails here: RUN pip3 install git+https://github.com/maxbachmann/RapidFuzz

RapidFuzz has been the most promising fuzzy matcher in my findings with .cdist()

RapidFuzz is a faster implementation.

For fuzzy matching - there's Rapidfuzz.

There is also https://github.com/maxbachmann/RapidFuzz which uses the MIT license.

RapidFuzz

probbaly can use https://github.com/seatgeek/thefuzz

Check out https://github.com/seatgeek/thefuzz. It basically provides different methods that take two strings and return a score between 0 and 100 indicating how similar they are. For instance,

Consider something like Levenshtein distance and one of it's implementations like thefuzz.

The problem you are solving is most commonly called “fuzzy string matching”. There are a bunch of algorithms for it (some of which are described in this thread) depending on your specific requirements. I’d start with an existing fuzzy string matching library (e.g. thefuzz, for python) and calculate matches between your input letter cases and your list of names. This sounds pretty reasonable to do fast since fuzzy string matching is commonly used in text editors to make it easier to find files. If you start with a fuzzy string matching library, I wouldn’t worry about asymptomatic complexity until you actually see a performance problem.

You probably want a package that can do fuzzy matching. The first search result for me turned up this: https://github.com/seatgeek/thefuzz

Having said that, you can use the Levenshtein distance to compute how many "edits" (substitutions, deletions, insertions) the generated summary is away from the original abstract. The package TheFuzz implements this concept in Python. For example fuzz.ratio(text1, text2) will give you a similarity score.

fuzzywuzzy is actually just called the thefuzz now.

It sounds like what you're looking for is "fuzzy string matching," which is not just checking if a string matches another exactly, but defining a way to measure "how close" a string is to another. Luckily, it looks like there's a good Python library for that already: https://github.com/seatgeek/thefuzz

Finally, even though Tesseract's output is usually very nice, it can sometime make a mistake. Again, this is case-specific, and if you're extracting for example numbers, it will be very hard to check for errors, but since I'm extracting names, I'm capable of fuzzy comparing the names detected by Slavic NER to a database of names that I have. I do this fuzzy matching with thefuzz library, and in cases I find a very high match with one of the names in my database, I simply fix the error by taking the name from there.