Cthulhu

Cthulhu is a lightweight Python library for evaluating the quality of HTR (Handwritten Text Recognition) and OCR (Optical Character Recognition) transcriptions.

It compares a ground truth against a prediction and returns a set of standard metrics (WER, CER, Levenshtein distance, etc.), with optional text normalisation transforms applied before scoring.

Originally derived from kami-lib — redesigned as a standalone evaluation toolkit with no deep-learning dependencies.

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Features

• Flexible input — accepts plain strings, .txt files, ALTO XML, and PAGE-XML
• Rich metrics — WER, CER, WACC, MER, CIP, CIL, ROUGE-1/2/L, Levenshtein, Hamming, weighted variants
• Text transforms — normalise both sequences before scoring (remove digits, punctuation, diacritics, change case)
• No heavy dependencies — no Kraken, no PyTorch; pure Python + three lightweight packages

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Installation

pip install cthulhu-eval

Dependencies:

Package	Role
python-Levenshtein >= 0.21	Fast C-extension for edit distance
Unidecode >= 1.3	Diacritics removal
termcolor >= 1.1	Coloured log output

Python: 3.9 or later

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Quick start

from cthulhu.Cthulhu import Cthulhu

# Two plain strings
k = Cthulhu(["ground truth text", "predicted text"])
print(k.scores.board)   # full metrics dict
print(k.scores.wer)     # word error rate
print(k.scores.cer)     # character error rate

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Input formats

data must be a list of exactly two elements. Each element can be:

Type	Example
Plain string	"mon texte de référence"
Text file	"./gt.txt"
ALTO XML (v2–v4)	"./document_alto.xml"
PAGE-XML (PcGts)	"./document_page.xml"

Mix and match freely:

# XML ground truth vs plain-text prediction
k = Cthulhu(["./gt_alto.xml", "./prediction.txt"])

# Two XML files
k = Cthulhu(["./gt_page.xml", "./pred_page.xml"])

# String vs text file
k = Cthulhu(["reference string", "./pred.txt"])

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Metrics

All metrics are available via k.scores.board (dict) or as individual attributes on k.scores.

Metric	Attribute	Description
Levenshtein distance (char)	lev_distance_char	Edit distance at character level
Levenshtein distance (word)	lev_distance_words	Edit distance at word level
Hamming distance	hamming	Char-level; "Ø" if lengths differ
WER	wer	Word Error Rate
CER	cer	Character Error Rate
WACC	wacc	Word Accuracy (1 − WER)
WER Hunt	wer_hunt	WER with halved insertion/deletion costs
MER	mer	Match Error Rate
CIP	cip	Character Information Preserved
CIL	cil	Character Information Lost
ROUGE-1	rouge_1	Unigram overlap (precision, recall, F1)
ROUGE-2	rouge_2	Bigram overlap (precision, recall, F1)
ROUGE-L	rouge_l	Longest Common Subsequence (precision, recall, F1)
Hits	hits	Matching characters
Substitutions	substs	Character substitutions
Deletions	deletions	Character deletions
Insertions	insertions	Character insertions

Custom error weights

k = Cthulhu(
    ["reference", "prediction"],
    insertion_cost=0.5,
    deletion_cost=0.5,
    substitution_cost=1.0,
)

Display options

k = Cthulhu(
    ["reference", "prediction"],
    percent=True,        # express rates as percentages (e.g. 17.2 instead of 0.172)
    truncate=True,       # truncate floats
    round_digits=".001", # precision
)

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Text transforms

Apply normalisation steps before computing metrics. Each transform is scored individually and all together, letting you see the impact of each choice.

k = Cthulhu(
    ["Déjà 13 fois, Maxime !", "deja 13 fois maxime"],
    apply_transforms="XP",  # remove diacritics + punctuation
)

# k.scores.board contains:
# {
#   "default":           {...},  # raw scores
#   "remove_diacritics": {...},  # after X only
#   "remove_punctuation":{...},  # after P only
#   "all_transforms":    {...},  # after X + P combined
#   "Length_reference":  ...,
#   "Total_diacritics_removed_from_reference": ...,
#   ...
# }

Transform codes:

Code	Name	Effect
D	Remove digits	"1871" → ""
U	Uppercase	"texte" → "TEXTE"
L	Lowercase	"TEXTE" → "texte"
P	Remove punctuation	"Bonjour !" → "Bonjour "
X	Remove diacritics	"étaient" → "etaient"

Codes can be combined freely: "XP", "DLP", "XPLU", etc.

You can also use the transformation classes directly:

from cthulhu.preprocessing.transformation import (
    ToCompose,
    RemoveDiacritics,
    RemovePunctuation,
    ToLowerCase,
    RemoveNonUsefulWords,
    RemoveDigits,
    RemoveSpecificWords,
    SubRegex,
    Strip,
)

# Apply a chain of transforms to any string or list of strings
result = ToCompose(
    ["Déjà 13 fois, Maxime !", ""],
    [RemoveDiacritics(), RemovePunctuation(), ToLowerCase(), RemoveNonUsefulWords()]
)
print(result.reference)  # "deja 13 fois maxime"

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Using Scorer directly

For lower-level access, use the Scorer class without the Cthulhu facade:

from cthulhu.metrics.evaluation import Scorer

scorer = Scorer(
    reference="Six semaines plus tard",
    prediction="Six semaiNEs plus tard",
    show_percent=True,
    truncate_score=True,
    round_digits=".001",
)

print(scorer.wer)
print(scorer.cer)
print(scorer.board)

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Project structure

cthulhu/
├── cthulhu/
│   ├── Cthulhu.py           # Main facade class
│   ├── metrics/
│   │   ├── _base_metrics.py # Encoding helpers, rounding utilities
│   │   └── evaluation.py    # Scorer class
│   ├── parser/
│   │   ├── parser_xml.py    # ALTO / PAGE-XML parser (stdlib only)
│   │   └── parser_text.py   # Plain-text file reader
│   ├── preprocessing/
│   │   └── transformation.py # Transform classes
│   └── utils/
│       └── _utils.py        # Logging, timing decorator
├── tests/
├── datatest/
├── requirements.txt
└── setup.py

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Running tests

python -m pytest tests/ -v

19 tests, ~0.06 s.

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Roadmap

• API-based HTR inference — send an image to an external model endpoint and compare the result against a ground-truth XML, without any local model

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Authors & licence

Original work by Alix Chagué and Lucas Terriel (Inria). MIT Licence.