hunspell VS SymSpell

i prefer and use hunspell

One can implement Huntspell which is what all browsers use (for example when typing in text areas). Is very simple and is C++.

Hunspell-based spell check

Excellent questions. https://github.com/wooorm/dictionaries here are some. The original dataset is here https://github.com/hunspell/hunspell#dictionaries

Since you are using already GPLv3: Why not reusing hunspell dictionaries/wordbooks? https://github.com/hunspell/hunspell

Those questions are open ones—and even the way they can be answered is unclear. Intuitively, Hunspell's suggestions are quite decent—otherwise, it wouldn't be the most widespread spellchecker, after all. A fair amount of "unhappy customers" can be easily found, too, in hunspell's repo issues. At the same time, one should distinguish between different reasons for the sub-par suggestion quality. It might be due to the algorithm itself, or due to the source data quality: the literal absence of the desired suggestion in the dictionary, or lack of aff-file settings that could've guided Hunspell to finding it.

Currently, Hunspell is maintained on GitHub (repo has only around 1k stars, will you believe it?). It seems that maintenance is not that easy if you'll weight the number of open issues and PRs, and the latest commits timeline: at the time of writing it (Jan 2021), the last commit to master was of May 2020, and the last release was 1.7 on Dec 2018. Hunspell's codebase is mostly "old-school" C++. It is being slowly modernized and it has very few comments; there are thousands of two-branch ifs to handle non-Unicode and Unicode text separately. There is also an attempt to rewrite Hunspell from scratch in a modern C++, which at some point was developed under the hunspell GitHub organization. Now it is independent and called nuspell (and, while not yet supporting all of the Hunspell features, already "achieved" version 4.2.0).

SymSpell: 1M times faster spelling correction\ (6 comments)

SymSpell

I'm glad to see these built-in to Postgres, as these are the basics of fuzzy string matching.

A quantum leap would be to integrate an implementation of the symmetric delete algorithm, such as https://github.com/wolfgarbe/SymSpell

Soundex and Phonex can yield too many false negatives outside of phonetically English names. Levenshtein/Jaro-Winkler aren't indexable solutions themselves, so they require N^2 comparisons. SymSpell conceptually combines these two into an indexed string-distance solution. It has the usual index issue of being designed for many reads, few writes.

Some of the modern approaches to spellchecking still take this road: for example, SymSpell algorithm (claiming to be "1 million times faster") is at its core just a brilliant idea for a novel storage format for a flat word list, that allows optimizing the calculation of edit distance significantly.

https://github.com/wolfgarbe/SymSpell lists 5 JS implementations (+ a Rust one that compiles to web assembly)