Analiticcl is an approximate string matching or fuzzy-matching system that can be used to find variants for spelling correction or text normalisation
Analiticcl is an approximate string matching or fuzzy-matching system that can be used for spelling correction or text normalisation (such as post-OCR correction or post-HTR correction). Texts can be checked against a validated or corpus-derived lexicon (with or without frequency information) and spelling variants will be returned.
Please see the main README.md for a further introduction, it also links to a Python tutorial.
Analiticcl is written in Rust, this is the Python binding, allowing you to use analiticcl from Python as a module.
pip install analiticcl
To use this method, you need to have Rust installed and in your $PATH. Install it through your package manager or through rustup:
curl https://sh.rustup.rs -sSf | sh -s -- -y
export PATH="$HOME/.cargo/bin:$PATH"
Once Rust is installed, you can compile the analiticcl binding:
# Create a virtual env (you can use yours as well)
python -m venv .env
source .env/bin/activate
# Install `analiticcl` in the current virtual env
pip install setuptools_rust
python setup.py install
from analiticcl import VariantModel, Weights, SearchParameters
import json
model = VariantModel("examples/simple.alphabet.tsv", Weights(), debug=False)
model.read_lexicon("examples/eng.aspell.lexicon")
model.build()
result = model.find_variants("udnerstand", SearchParameters(max_edit_distance=3))
print(json.dumps(result, ensure_ascii=False, indent=4))
print()
results = model.find_all_matches("I do not udnerstand the probleem", SearchParameters(max_edit_distance=3,max_ngram=1))
print(json.dumps(results, ensure_ascii=False, indent=4))
Note: all offsets reported by analiticcl are utf-8 byte-offsets, not character offsets! If you want proper unicode character
offsets, pass the keyword argument unicodeoffset=True to SearchParameters. You will want to set this if you intend to do
any kind of slicing in Python (which uses unicode points by default).
Output:
[
{
"text": "understand",
"score": 0.8978494623655915,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understands",
"score": 0.6725317693059629,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understood",
"score": 0.6036866359447004,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understate",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
[
{
"input": "I",
"offset": {
"begin": 0,
"end": 1
},
"variants": [
{
"text": "I",
"score": 0.8387096774193549,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "i",
"score": 0.8064516129032258,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "do",
"offset": {
"begin": 2,
"end": 4
},
"variants": [
{
"text": "do",
"score": 1.0,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "dog",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "doc",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "doz",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "dob",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "doe",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "dot",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "dos",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "ado",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "don",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "d",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "o",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "DOD",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "not",
"offset": {
"begin": 5,
"end": 8
},
"variants": [
{
"text": "not",
"score": 1.0,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "knot",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "note",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "snot",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "no",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "nowt",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "No",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "OT",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "pot",
"score": 0.5698924731182795,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "udnerstand",
"offset": {
"begin": 9,
"end": 19
},
"variants": [
{
"text": "understand",
"score": 0.8978494623655915,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understands",
"score": 0.6725317693059629,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understood",
"score": 0.6036866359447004,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "understate",
"score": 0.5967741935483871,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "the",
"offset": {
"begin": 20,
"end": 23
},
"variants": [
{
"text": "the",
"score": 1.0,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "thee",
"score": 0.6908602150537635,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "thew",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "then",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "them",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "they",
"score": 0.6370967741935484,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "he",
"score": 0.6236559139784946,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Thea",
"score": 0.6048387096774194,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Th",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "He",
"score": 0.5913978494623655,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "thy",
"score": 0.5698924731182795,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "she",
"score": 0.5698924731182795,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "tho",
"score": 0.5698924731182795,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Thu",
"score": 0.5376344086021505,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Che",
"score": 0.5376344086021505,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "THC",
"score": 0.5376344086021505,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "tee",
"score": 0.5161290322580645,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "toe",
"score": 0.5161290322580645,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "tie",
"score": 0.5161290322580645,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "Te",
"score": 0.510752688172043,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
},
{
"input": "probleem",
"offset": {
"begin": 24,
"end": 32
},
"variants": [
{
"text": "problem",
"score": 0.9231950844854071,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "problems",
"score": 0.6908602150537635,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "probe",
"score": 0.5913978494623656,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "proclaim",
"score": 0.5766129032258065,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "probated",
"score": 0.543010752688172,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "probates",
"score": 0.543010752688172,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "prole",
"score": 0.5322580645161291,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "prowlers",
"score": 0.4959677419354839,
"lexicon": "../../../examples/eng.aspell.lexicon"
},
{
"text": "parolees",
"score": 0.44220430107526887,
"lexicon": "../../../examples/eng.aspell.lexicon"
}
]
}
]
The python binding exposes only a minimal interface, you can use Python's help() function to get information on the
classes provided. For more detailed information, please consult the Analiticcl's rust API documentation. The interfaces that are available in the binding are analogous to the rust versions.
FAQs
Analiticcl is an approximate string matching or fuzzy-matching system that can be used to find variants for spelling correction or text normalisation
We found that analiticcl demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
An advanced npm supply chain attack is leveraging Ethereum smart contracts for decentralized, persistent malware control, evading traditional defenses.
Security News
Research
Attackers are impersonating Sindre Sorhus on npm with a fake 'chalk-node' package containing a malicious backdoor to compromise developers' projects.
Security News
The npm package for the LottieFiles Player web component was hit with a supply chain attack after a software engineer's npmjs credentials were compromised.