SymSpell<br>
[](https://crates.io/crates/symspell_rs)
[](https://crates.io/crates/symspell_rs)
[](https://github.com/wolfgarbe/symspell_rs/blob/master/LICENSE)
[](https://docs.rs/symspell_rs)
========
**Rust implementation of [SymSpell](https://github.com/wolfgarbe/SymSpell) originally written in C#.**
Spelling correction & Fuzzy search: **1 million times faster** through Symmetric Delete spelling correction algorithm
The Symmetric Delete spelling correction algorithm reduces the complexity of edit candidate generation and dictionary lookup for a given Damerau-Levenshtein distance. It is six orders of magnitude faster ([than the standard approach with deletes + transposes + replaces + inserts](http://norvig.com/spell-correct.html)) and language independent.
Opposite to other algorithms only deletes are required, no transposes + replaces + inserts.
Transposes + replaces + inserts of the input term are transformed into deletes of the dictionary term.
Replaces and inserts are expensive and language dependent: e.g. Chinese has 70,000 Unicode Han characters!
The speed comes from the inexpensive **delete-only edit candidate generation** and the **pre-calculation**.<br>
An average 5 letter word has about **3 million possible spelling errors** within a maximum edit distance of 3,<br>
but SymSpell needs to generate **only 25 deletes** to cover them all, both at pre-calculation and at lookup time. Magic!
If you like SymSpell, try [**SeekStorm**](https://github.com/SeekStorm/SeekStorm) - a sub-millisecond full-text search library & multi-tenancy server in Rust (Open Source).
<br>
```text
Copyright (c) 2025 Wolf Garbe
Version: 6.7.3
Author: Wolf Garbe <wolf.garbe@seekstorm.com>
Maintainer: Wolf Garbe <wolf.garbe@seekstorm.com>
URL: https://github.com/wolfgarbe/symspell
Description: https://seekstorm.com/blog/1000x-spelling-correction/
MIT License
Copyright (c) 2025 Wolf Garbe
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
https://opensource.org/licenses/MIT
```
---
## Single word spelling correction
**Lookup** provides a very fast spelling correction of single words.
* A **Verbosity parameter** allows to control the number of returned results:<br>
Top: Top suggestion with the highest term frequency of the suggestions of smallest edit distance found.<br>
Closest: All suggestions of smallest edit distance found, suggestions ordered by term frequency.<br>
All: All suggestions within maxEditDistance, suggestions ordered by edit distance, then by term frequency.
* The **Maximum edit distance parameter** controls up to which edit distance words from the dictionary should be treated as suggestions.
* The required **Word frequency dictionary** can either be directly loaded from text files (**LoadDictionary**) or generated from a large text corpus (**CreateDictionary**).
#### Applications
* Spelling correction,
* Query correction (10–15% of queries contain misspelled terms),
* Chatbots,
* OCR post-processing,
* Automated proofreading.
* Fuzzy search & approximate string matching
#### Performance (single term)
0.033 milliseconds/word (edit distance 2) and 0.180 milliseconds/word (edit distance 3) (single core on 2012 Macbook Pro)<br>
<br><br>
**1,870 times faster than [BK-tree](https://en.wikipedia.org/wiki/BK-tree)** (see [Benchmark 1](https://seekstorm.com/blog/symspell-vs-bk-tree/): dictionary size=500,000, maximum edit distance=3, query terms with random edit distance = 0...maximum edit distance, verbose=0)<br><br>
**1 million times faster than [Norvig's algorithm](http://norvig.com/spell-correct.html)** (see [Benchmark 2](http://blog.faroo.com/2015/03/24/fast-approximate-string-matching-with-large-edit-distances/): dictionary size=29,157, maximum edit distance=3, query terms with fixed edit distance = maximum edit distance, verbose=0)<br>
#### Blog Posts: Algorithm, Benchmarks, Applications
[1000x Faster Spelling Correction algorithm](https://seekstorm.com/blog/1000x-spelling-correction/)<br>
[Fast approximate string matching with large edit distances in Big Data](https://seekstorm.com/blog/fast-approximate-string-matching/)<br>
[Very fast Data cleaning of product names, company names & street names](https://seekstorm.com/blog/very-data-cleaning-of-product-names-company-names-street-names/)<br>
[Sub-millisecond compound aware automatic spelling correction](https://seekstorm.com/blog/sub-millisecond-compound-aware-automatic.spelling-correction/)<br>
[SymSpell vs. BK-tree: 100x faster fuzzy string search & spell checking](https://seekstorm.com/blog/symspell-vs-bk-tree/)<br>
[Fast Word Segmentation for noisy text](https://seekstorm.com/blog/fast-word-segmentation-noisy-text/)<br>
[The Pruning Radix Trie — a Radix trie on steroids](https://seekstorm.com/blog/pruning-radix-trie/)<br>
---
## Compound aware multi-word spelling correction
**LookupCompound** supports __compound__ aware __automatic__ spelling correction of __multi-word input__ strings.
__1. Compound splitting & decompounding__
Lookup() assumes every input string as _single term_. LookupCompound also supports _compound splitting / decompounding_ with three cases:
1. mistakenly __inserted space within a correct word__ led to two incorrect terms
2. mistakenly __omitted space between two correct words__ led to one incorrect combined term
3. __multiple input terms__ with/without spelling errors
Splitting errors, concatenation errors, substitution errors, transposition errors, deletion errors and insertion errors can by mixed within the same word.
__2. Automatic spelling correction__
* Large document collections make manual correction infeasible and require unsupervised, fully-automatic spelling correction.
* In conventional spelling correction of a single token, the user is presented with multiple spelling correction suggestions. <br>For automatic spelling correction of long multi-word text the algorithm itself has to make an educated choice.
__Examples:__
```diff
- whereis th elove hehad dated forImuch of thepast who couqdn'tread in sixthgrade and ins pired him
+ where is the love he had dated for much of the past who couldn't read in sixth grade and inspired him (9 edits)
- in te dhird qarter oflast jear he hadlearned ofca sekretplan
+ in the third quarter of last year he had learned of a secret plan (9 edits)
- the bigjest playrs in te strogsommer film slatew ith plety of funn
+ the biggest players in the strong summer film slate with plenty of fun (9 edits)
- Can yu readthis messa ge despite thehorible sppelingmsitakes
+ can you read this message despite the horrible spelling mistakes (9 edits)
```
#### Performance (compounds)
0.2 milliseconds / word (edit distance 2)
5000 words / second (single core on 2012 Macbook Pro)
---
## Word Segmentation of noisy text
**WordSegmentation** divides a string into words by inserting missing spaces at appropriate positions.<br>
* Misspelled words are corrected and do not prevent segmentation.<br>
* Existing spaces are allowed and considered for optimum segmentation.<br>
* SymSpell.WordSegmentation uses a [**Triangular Matrix approach**](https://seekstorm.com/blog/fast-word-segmentation-noisy-text/) instead of the conventional Dynamic Programming: It uses an array instead of a dictionary for memoization, loops instead of recursion and incrementally optimizes prefix strings instead of remainder strings.<br>
* The Triangular Matrix approach is faster than the Dynamic Programming approach. It has a lower memory consumption, better scaling (constant O(1) memory consumption vs. linear O(n)) and is GC friendly.
* While each string of length n can be segmented into **2^n−1** possible [compositions](https://en.wikipedia.org/wiki/Composition_(combinatorics)),<br>
SymSpell.WordSegmentation has a **linear runtime O(n)** to find the optimum composition.
__Examples:__
```diff
- thequickbrownfoxjumpsoverthelazydog
+ the quick brown fox jumps over the lazy dog
- itwasabrightcolddayinaprilandtheclockswerestrikingthirteen
+ it was a bright cold day in april and the clocks were striking thirteen
- itwasthebestoftimesitwastheworstoftimesitwastheageofwisdomitwastheageoffoolishness
+ it was the best of times it was the worst of times it was the age of wisdom it was the age of foolishness
```
__Applications:__
* Word Segmentation for CJK languages for Indexing Spelling correction, Machine translation, Language understanding, Sentiment analysis
* Normalizing English compound nouns for search & indexing (e.g. ice box = ice-box = icebox; pig sty = pig-sty = pigsty)
* Word segmentation for compounds if both original word and split word parts should be indexed.
* Correction of missing spaces caused by Typing errors.
* Correction of Conversion errors: spaces between word may get lost e.g. when removing line breaks.
* Correction of OCR errors: inferior quality of original documents or handwritten text may prevent that all spaces are recognized.
* Correction of Transmission errors: during the transmission over noisy channels spaces can get lost or spelling errors introduced.
* Keyword extraction from URL addresses, domain names, #hashtags, table column descriptions or programming variables written without spaces.
* For password analysis, the extraction of terms from passwords can be required.
* For Speech recognition, if spaces between words are not properly recognized in spoken language.
* Automatic CamelCasing of programming variables.
* Applications beyond Natural Language processing, e.g. segmenting DNA sequence into words
__Performance:__
4 milliseconds for segmenting an 185 char string into 53 words (single core on 2012 Macbook Pro)
<br>
---
#### Usage of SymSpell Library
Single word spelling correction
```rust
use symspell_rs::{SymSpell, Verbosity};
use std::path::Path;
let max_edit_distance_dictionary = 2; //maximum edit distance per dictionary precalculation
let mut symspell: SymSpell = SymSpell::new(max_edit_distance_dictionary,None, 7, 1);
// single term dictionary
let term_index = 0; //column of the term in the dictionary text file
let count_index = 1; //column of the term frequency in the dictionary text file
symspell.load_dictionary(Path::new("data/frequency_dictionary_en_82_765.txt"), term_index, count_index, " ");
//lookup suggestions for single-word input strings
let input_term = "hous";
let suggestion_verbosity = Verbosity::Closest;//Top, Closest, All
let max_edit_distance_lookup = 1; //max edit distance per lookup (maxEditDistanceLookup<=maxEditDistanceDictionary)
let suggestions = symspell.lookup(input_term, suggestion_verbosity, max_edit_distance_lookup,&None,None,false);
//display suggestions, edit distance and term frequency
println!("{:?}", suggestions);
```
Compound aware multi-word spelling correction
```rust
use symspell_rs::{SymSpell, Verbosity};
use std::path::Path;
let max_edit_distance_dictionary = 2;
let mut symspell = SymSpell::new(max_edit_distance_dictionary,None, 7, 1);
// single term dictionary
let term_index = 0; //column of the term in the dictionary text file
let count_index = 1; //column of the term frequency in the dictionary text file
symspell.load_dictionary(Path::new("data/frequency_dictionary_en_82_765.txt"), term_index, count_index, " ");
// bigram dictionary
symspell.load_bigram_dictionary(Path::new("data/frequency_bigramdictionary_en_243_342.txt"),0,2, " ",
);
//lookup suggestions for multi-word input strings (supports compound splitting & merging)
let input_sentence = "whereis th elove hehad dated forImuch of thepast who couqdn'tread in sixtgrade and ins pired him";
let max_edit_distance_lookup = 2; //max edit distance per lookup (per single word, not per whole input string)
let compound_suggestions = symspell.lookup_compound(input_sentence, max_edit_distance_lookup,&None,false);
//display suggestions, edit distance and term frequency
println!("{:?}", compound_suggestions);
```
Word Segmentation of noisy text
```rust
use symspell_rs::{SymSpell, Verbosity};
use std::path::Path;
let max_edit_distance_dictionary = 0; //maximum edit distance per dictionary precalculation
let mut symspell = SymSpell::new(max_edit_distance_dictionary,None, 7, 1);
// single term dictionary
let term_index = 0; //column of the term in the dictionary text file
let count_index = 1; //column of the term frequency in the dictionary text file
symspell.load_dictionary(Path::new("data/frequency_dictionary_en_82_765.txt"), term_index, count_index, " ");
//word segmentation and correction for multi-word input strings with/without spaces
let input_sentence = "thequickbrownfoxjumpsoverthelazydog";
let max_edit_distance_lookup = 0;
let result = symspell.word_segmentation(input_sentence, max_edit_distance_lookup);
//display term and edit distance
println!("{:?}", result.segmented_string);
```
Word Segmentation of Chinese text
```rust
use symspell_rs::{SymSpell, Verbosity};
use std::path::Path;
let max_edit_distance_dictionary = 0; //maximum edit distance per dictionary precalculation
let mut symspell = SymSpell::new(max_edit_distance_dictionary,None, 7, 1);
// single term dictionary
let term_index = 0; //column of the term in the dictionary text file
let count_index = 1; //column of the term frequency in the dictionary text file
symspell.load_dictionary(Path::new("data/frequency_dictionary_zh_cn_349_045.txt"), term_index, count_index, " ");
//word segmentation and correction for multi-word input strings with/without spaces
let input_sentence = "部分居民生活水平";
let max_edit_distance_lookup = 0;
let result = symspell.word_segmentation(input_sentence, max_edit_distance_lookup);
//display term and edit distance
println!("{:?}", result.segmented_string);
```
---
#### Frequency dictionary
Dictionary quality is paramount for correction quality. In order to achieve this two data sources were combined by intersection: Google Books Ngram data which provides representative word frequencies (but contains many entries with spelling errors) and SCOWL — Spell Checker Oriented Word Lists which ensures genuine English vocabulary (but contained no word frequencies required for ranking of suggestions within the same edit distance).
The [frequency_dictionary_en_82_765.txt](https://github.com/wolfgarbe/SymSpell/blob/master/SymSpell/frequency_dictionary_en_82_765.txt) was created by intersecting the two lists mentioned below. By reciprocally filtering only those words which appear in both lists are used. Additional filters were applied and the resulting list truncated to ≈ 80,000 most frequent words.
* [Google Books Ngram data](http://storage.googleapis.com/books/ngrams/books/datasetsv2.html) [(License)](https://creativecommons.org/licenses/by/3.0/) : Provides representative word frequencies
* [SCOWL - Spell Checker Oriented Word Lists](http://wordlist.aspell.net/) [(License)](http://wordlist.aspell.net/scowl-readme/) : Ensures genuine English vocabulary
#### Dictionary file format
* Plain text file in UTF-8 encoding - without Byte Order Mark (BOM)!.
* Word and Word Frequency are separated by space or tab. Per default, the word is expected in the first column and the frequency in the second column. But with the termIndex and countIndex parameters in LoadDictionary() the position and order of the values can be changed and selected from a row with more than two values. This allows to augment the dictionary with additional information or to adapt to existing dictionaries without reformatting.
* Every word-frequency-pair in a separate line. A line is defined as a sequence of characters followed by a line feed ("\n"), a carriage return ("\r"), or a carriage return immediately followed by a line feed ("\r\n").
* Both dictionary terms and input term are expected to be in **lower case**.
You can build your own frequency dictionary for your language or your specialized technical domain.
The SymSpell spelling correction algorithm supports languages with non-latin characters, e.g Cyrillic, Chinese or [Georgian](https://github.com/irakli97/Frequency_Dictionary_GE_363_202).
#### Frequency dictionaries in other languages
SymSpell includes an [English frequency dictionary](https://github.com/wolfgarbe/SymSpell/blob/master/SymSpell/frequency_dictionary_en_82_765.txt)
Dictionaries for Chinese, English, French, German, Hebrew, Italian, Russian and Spanish are located here:<br>
[SymSpell.FrequencyDictionary](https://github.com/wolfgarbe/SymSpell/tree/master/SymSpell.FrequencyDictionary)
Frequency dictionaries in many other languages can be found here:<br>
[FrequencyWords repository](https://github.com/hermitdave/FrequencyWords)<br>
[Frequency dictionaries](https://github.com/dataiku/dss-plugin-nlp-preparation/tree/master/resource/dictionaries)<br>
[Frequency dictionaries](https://github.com/LuminosoInsight/wordfreq/tree/master/wordfreq/data)
N-Gram Generator by repetitio:<br>
This repository contains a script to generate unigrams and bigrams from Wikipedias dataset from HuggingFace for the use with the SymSpell.<br>
https://gitlab.com/repetitio/utils/ngram-frequencies/-/tree/main?ref_type=heads
---
### Official implementations
**C#**<br>
https://github.com/wolfgarbe/symspell
**NuGet package**<br>
https://www.nuget.org/packages/symspell
**Rust**<br>
https://github.com/wolfgarbe/symspell_rs
**Crates.io**<br>
https://crates.io/crates/symspell_rs
### Ports
The following third party ports or reimplementations to other programming languages have not been tested by myself whether they are an exact port, error free, provide identical results or are as fast as the original algorithm.
Older ports target SymSpell **version 3.0**. But **version 6.1.** provides **much higher speed & lower memory consumption!**
**WebAssembly**<br>
https://github.com/justinwilaby/spellchecker-wasm<br>
**WEB API (Docker)**<br>
https://github.com/LeonErath/SymSpellAPI (Version 6.3)<br>
**C**<br>
https://github.com/sumanpokhrel-11/symspell-c99<br>
**C++**<br>
https://github.com/AtheS21/SymspellCPP (Version 6.5)<br>
https://github.com/erhanbaris/SymSpellPlusPlus (Version 6.1)
**C#**<br>
https://github.com/wolfgarbe/symspell
**Crystal**<br>
https://github.com/chenkovsky/aha/blob/master/src/aha/sym_spell.cr
**Go**<br>
https://github.com/snapp-incubator/go-symspell<br>
https://github.com/sajari/fuzzy<br>
https://github.com/eskriett/spell<br>
https://github.com/Trendyol/go-symspell
**Haskell**<br>
https://github.com/cbeav/symspell
**Java**<br>
https://github.com/MighTguY/customized-symspell (Version 6.6)<br>
https://github.com/rxp90/jsymspell (Version 6.6)<br>
https://github.com/Lundez/JavaSymSpell (Version 6.4)<br>
https://github.com/rxp90/jsymspell<br>
https://github.com/gpranav88/symspell<br>
https://github.com/searchhub/preDict<br>
https://github.com/jpsingarayar/SpellBlaze
**Javascript**<br>
https://github.com/MathieuLoutre/node-symspell (Version 6.6, needs Node.js)<br>
https://github.com/itslenny/SymSpell.js<br>
https://github.com/dongyuwei/SymSpell<br>
https://github.com/IceCreamYou/SymSpell<br>
https://github.com/Yomguithereal/mnemonist/blob/master/symspell.js
**Julia**<br>
https://github.com/Arkoniak/SymSpell.jl
**Kotlin**<br>
https://github.com/Wavesonics/SymSpellKt
**Objective-C**<br>
https://github.com/AmitBhavsarIphone/SymSpell (Version 6.3)
**PHP**<br>
https://github.com/Jakhotiya/symspell-php
**Python**<br>
https://github.com/mammothb/symspellpy (Version 6.7)<br>
https://github.com/viig99/SymSpellCppPy (Version 6.5)<br>
https://github.com/zoho-labs/symspell (Python bindings of Rust version)<br>
https://github.com/ne3x7/pysymspell/ (Version 6.1)<br>
https://github.com/Ayyuriss/SymSpell<br>
https://github.com/ppgmg/github_public/blob/master/spell/symspell_python.py<br>
https://github.com/rcourivaud/symspellcompound<br>
https://github.com/Esukhia/sympound-python<br>
https://www.kaggle.com/yk1598/symspell-spell-corrector
**Ruby**<br>
https://github.com/PhilT/symspell<br>
https://github.com/scientist-labs/spellkit
**Rust**<br>
https://github.com/wolfgarbe/symspell_rs (Version 6.7.3)<br>
https://github.com/reneklacan/symspell (Version 6.6, compiles to WebAssembly)<br>
https://github.com/luketpeterson/fuzzy_rocks (persistent datastore backed by RocksDB)<br>
https://github.com/daibo83/fast_symspell<br>
https://github.com/Saphereye/symspellrs
**Scala**<br>
https://github.com/semkath/symspell
**Swift**<br>
https://github.com/gdetari/SymSpellSwift
**Zig**<br>
https://github.com/alim-zanibekov/miara?tab=readme-ov-file#symspell
---
### Citations
Contextual Multilingual Spellchecker for User Queries<br>
Sanat Sharma, Josep Valls-Vargas, Tracy Holloway King, Francois Guerin, Chirag Arora (Adobe)<br>
https://arxiv.org/abs/2305.01082
A context sensitive real-time Spell Checker with language adaptability<br>
Prabhakar Gupta (Amazon)<br>
https://arxiv.org/abs/1910.11242
SpeakGer: A meta-data enriched speech corpus of German state and federal parliaments<br>
Kai-Robin Lange and Carsten Jentsch<br>
https://arxiv.org/pdf/2410.17886
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction<br>
Stuart Mesham, Christopher Bryant, Marek Rei, Zheng Yuan<br>
https://arxiv.org/abs/2302.05913
German Parliamentary Corpus (GERPARCOR)<br>
Giuseppe Abrami, Mevlüt Bagci, Leon Hammerla, Alexander Mehler<br>
https://arxiv.org/abs/2204.10422
iOCR: Informed Optical Character Recognition for Election Ballot Tallies<br>
Kenneth U. Oyibo, Jean D. Louis, Juan E. Gilbert<br>
https://arxiv.org/abs/2208.00865
Amazigh spell checker using Damerau-Levenshtein algorithm and N-gram<br>
Youness Chaabi, Fadoua Ataa Allah<br>
https://www.sciencedirect.com/science/article/pii/S1319157821001828
Survey of Query correction for Thai business-oriented information retrieval<br>
Phongsathorn Kittiworapanya, Nuttapong Saelek, Anuruth Lertpiya, Tawunrat Chalothorn<br>
https://ieeexplore.ieee.org/document/9376809
SymSpell and LSTM based Spell- Checkers for Tamil<br>
Selvakumar MuruganTamil Arasan BakthavatchalamTamil Arasan BakthavatchalamMalaikannan Sankarasubbu<br>
https://www.researchgate.net/publication/349924975_SymSpell_and_LSTM_based_Spell-_Checkers_for_Tamil
SymSpell4Burmese: Symmetric Delete Spelling Correction Algorithm (SymSpell) for Burmese Spelling Checking<br>
Ei Phyu Phyu Mon; Ye Kyaw Thu; Than Than Yu; Aye Wai Oo<br>
https://ieeexplore.ieee.org/document/9678171
Spell Check Indonesia menggunakan Norvig dan SymSpell<br>
Yasir Abdur Rohman<br>
https://medium.com/@yasirabd/spell-check-indonesia-menggunakan-norvig-dan-symspell-4fa583d62c24
Analisis Perbandingan Metode Burkhard Keller Tree dan SymSpell dalam Spell Correction Bahasa Indonesia<br>
Muhammad Hafizh Ferdiansyah, I Kadek Dwi Nuryana<br>
https://ejournal.unesa.ac.id/index.php/jinacs/article/download/50989/41739
Improving Document Retrieval with Spelling Correction for Weak and Fabricated Indonesian-Translated Hadith<br>
Muhammad zaky ramadhanKemas M LhaksmanaKemas M Lhaksmana<br>
https://www.researchgate.net/publication/342390145_Improving_Document_Retrieval_with_Spelling_Correction_for_Weak_and_Fabricated_Indonesian-Translated_Hadith
Symspell을 이용한 한글 맞춤법 교정<br>
김희규<br>
https://heegyukim.medium.com/symspell%EC%9D%84-%EC%9D%B4%EC%9A%A9%ED%95%9C-%ED%95%9C%EA%B8%80-%EB%A7%9E%EC%B6%A4%EB%B2%95-%EA%B5%90%EC%A0%95-3def9ca00805
Mending Fractured Texts. A heuristic procedure for correcting OCR data<br>
Jens Bjerring-Hansen, Ross Deans Kristensen-McLachla2, Philip Diderichsen and Dorte Haltrup Hansen<br>
https://ceur-ws.org/Vol-3232/paper14.pdf
Towards the Natural Language Processing as Spelling Correction for Offline Handwritten Text Recognition Systems<br>
Arthur Flor de Sousa Neto; Byron Leite Dantas Bezerra; and Alejandro Héctor Toselli<br>
https://www.mdpi.com/2076-3417/10/21/7711
When to Use OCR Post-correction for Named Entity Recognition?<br>
Vinh-Nam Huynh, Ahmed Hamdi, Antoine Doucet<br>
https://hal.science/hal-03034484v1/
Automatic error Correction: Evaluating Performance of Spell Checker Tools<br>
A. Tolegenova<br>
https://journals.sdu.edu.kz/index.php/nts/article/view/690
ZHAW-CAI: Ensemble Method for Swiss German Speech to Standard German Text<br>
Malgorzata Anna Ulasik, Manuela Hurlimann, Bogumila Dubel, Yves Kaufmann,<br>
Silas Rudolf, Jan Deriu, Katsiaryna Mlynchyk, Hans-Peter Hutter, and Mark Cieliebak<br>
https://ceur-ws.org/Vol-2957/sg_paper3.pdf
Cyrillic Word Error Program Based on Machine Learning<br>
Battumur, K., Dulamragchaa, U., Enkhbat, S., Altanhuyag, L., & Tumurbaatar, P.<br>
https://mongoliajol.info/index.php/JIMDT/article/view/2661
Fast Approximate String Search for Wikification<br>
Szymon Olewniczak, Julian Szymanski<br>
https://www.iccs-meeting.org/archive/iccs2021/papers/127440334.pdf
RuMedSpellchecker: Correcting Spelling Errors for Natural Russian Language in Electronic Health Records Using Machine Learning Techniques<br>
Dmitrii Pogrebnoi, Anastasia Funkner, Sergey Kovalchuk<br>
https://link.springer.com/chapter/10.1007/978-3-031-36024-4_16
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction<br>
Stuart Mesham, Christopher Bryant, Marek Rei, Zheng Yuan<br>
https://aclanthology.org/2023.findings-eacl.119.pdf
Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookup<br>
Touchchai Chotisorayuth, Andreas Tiffeau-Mayer<br>
https://arxiv.org/html/2403.09010v1
Unveiling Disguised Toxicity: A Novel Pre-processing Module for Enhanced Content Moderation<br>
Johnny Chan, Yuming Li<br>
https://www.sciencedirect.com/science/article/pii/S2215016124001225
Beyond the dictionary attack: Enhancing password cracking efficiency through machine learning-induced mangling rules<br>
Radek Hranický, Lucia Šírová, Viktor Rucký<br>
https://www.sciencedirect.com/science/article/pii/S2666281725000046
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### Upcoming changes
1. Utilizing the [pigeonhole principle](https://en.wikipedia.org/wiki/Pigeonhole_principle) by partitioning both query and dictionary terms will result in 5x less memory consumption and 3x faster precalculation time.
2. Option to preserve case (upper/lower case) of input term.
3. Open source the code for creating custom frequency dictionaries in any language and size as intersection between Google Books Ngram data (Provides representative word frequencies) and SCOWL Spell Checker Oriented Word Lists (Ensures genuine English vocabulary).
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