opencc_purepy

opencc_purepy is a pure Python implementation of OpenCC (Open Chinese Convert), supporting conversion between Simplified, Traditional, Hong Kong, Taiwan, and Japanese Kanji.
It uses dictionary-based segmentation and mapping logic inspired by the original OpenCC.

🚩 Features

• Pure Python – no native dependencies
• Multiple Chinese locale conversions (Simplified, Traditional, HK, TW, JP)
• Punctuation style conversion (optional)
• Unicode compatibility normalization (CJK compatibility ideographs and extended allographs)
• Automatic code detection (Simplified/Traditional)
• CLI with Office document support (.docx, .xlsx, .pptx, .odt, .ods, .odp, .epub)

🐍 opencc_purepy requires Python 3.7 or later.

🔁 Supported Conversion Configs

📦 Installation

pip install opencc-purepy

🚀 Usage

Python

from opencc_purepy import OpenCC

text = "“春眠不觉晓，处处闻啼鸟。”"
opencc = OpenCC("s2t")
converted = opencc.convert(text, punctuation=True)
print(converted)  # 「春眠不覺曉，處處聞啼鳥。」

CLI

The default conversion configuration is s2t. Run any subcommand with --help to see the complete supported configuration and custom-dictionary slot lists.

Text File Conversion

python -m opencc_purepy convert -i input.txt -o output.txt -c s2t -p
# or, if installed as a script:
opencc-purepy convert -i input.txt -o output.txt -c s2t -p

Standard input and output are used when -i or -o is omitted:

echo "汉字转换" | opencc-purepy convert -c s2t

Use --in-enc and --out-enc for non-UTF-8 text files. DeToFu fallback can be enabled at a selected extension level, with an optional UTF-8 custom mapping file:

opencc-purepy convert -i input.txt -o output.txt -c s2t \
  --in-enc utf-8 --out-enc utf-8 --detofu ext-c

opencc-purepy convert -i input.txt -c s2t \
  --detofu all --detofu-file ./custom_detofu.txt

Use -n/--norm-compat to normalize CJK Compatibility Ideographs before conversion, or -E/--norm-compat-extended to apply the complete built-in Unicode CJK compatibility normalization. If both options are specified, --norm-compat-extended takes precedence:

opencc-purepy convert -i input.txt -o output.txt -c t2s --norm-compat

opencc-purepy convert -i input.txt -o output.txt -c t2s \
  --norm-compat-extended

Office Document Conversion subcommand (office)

Supports: .docx, .xlsx, .pptx, .odt, .ods, .odp, .epub

# Convert Word document with font preservation
opencc-purepy office -i example.docx -c t2s --keep-font

# Convert EPUB and auto-detect output name from input file extension
opencc-purepy office -i book.epub -c s2t

# Convert Excel and specify output path and format
opencc-purepy office -i sheet.xlsx -o result.xlsx -c s2tw --format xlsx

ℹ️ With office subcommand, the input is processed as an Office or EPUB document and OpenCC conversion is applied internally.

📚 Custom Dictionaries

opencc_purepy follows the OpenCC lexicon structure. Custom entries are loaded through existing OpenCC dictionary slots, such as DictSlot.STPhrases, DictSlot.TSPhrases, DictSlot.STPunctuations, and other OpenCC slots. There is no generic UserDict slot.

Dictionary slot mappings support both:

• DictSlot (recommended)
• string slot names such as "st_phrases" (backward compatible)

Raw TXT dictionary loading: OpenCC.from_dicts()

Use OpenCC.from_dicts() when you want to load OpenCC TXT dictionary files directly through DictionaryMaxlength.from_dicts(). With appends={...}, it loads the base TXT dictionaries first, then custom entries. Duplicate keys use late-comer wins, so custom entries override built-in entries. This is recommended for most users.

from opencc_purepy import DictSlot, OpenCC

cc = OpenCC.from_dicts(
    config="s2t",
    appends={
        DictSlot.STPhrases: "./UserDict.txt",
        DictSlot.TWVariantsPhrases: "./custom/TWVariantsPhrases.txt",
        DictSlot.HKVariantsPhrases: "./custom/HKVariantsPhrases.txt",
    },
)

print(cc.convert("帕兰蒂尔是一家公司"))

String slot names remain supported for compatibility:

from opencc_purepy import OpenCC

cc = OpenCC.from_dicts(
    config="s2t",
    appends={
        "st_phrases": "./UserDict.txt",
    },
)

The same appends={...} and overrides={...} arguments are also supported by DictionaryMaxlength.from_dicts() when you want to create and reuse a dictionary instance yourself.

Quick file API: OpenCC.from_dict_files()

Use OpenCC.from_dict_files() when you want a post-load custom-file API on top of the packaged JSON dictionaries. It loads dictionary_maxlength.json first, then applies each OpenCC-compatible custom file to the requested slot.

from opencc_purepy import DictSlot, OpenCC
from opencc_purepy.utils import CustomDictSpec

cc = OpenCC.from_dict_files(
    config="hk2sp",
    specs=[
        CustomDictSpec(DictSlot.HKPhrasesRev, "append", "./my_hk_dict.txt"),
    ],
)

print(cc.convert("這個細路哥很靈活"))

Each CustomDictSpec is slot, mode, path:

• slot: a DictSlot, such as DictSlot.HKPhrasesRev
• mode: "append" or "override"
• path: a UTF-8 OpenCC text dictionary file

Use "append" for user terms that should extend a built-in slot. Use "override" only when the file is a complete replacement for that slot.

Dictionary slot parsing

DictSlot.parse() accepts a DictSlot directly or parses enum-style, compact, and legacy underscore-separated string forms. DictSlot.canonical_name() returns the public enum-style slot name used by cross-language APIs and custom dictionary specifications; .value remains the legacy underscore-separated key.

from opencc_purepy import DictSlot, OpenCC

slot = DictSlot.parse("HKPhrasesRev")
assert slot is DictSlot.HKPhrasesRev
assert DictSlot.parse("hkphrasesrev") is slot
assert DictSlot.parse("hk_phrases_rev") is slot
assert slot.canonical_name() == "HKPhrasesRev"
assert slot.value == "hk_phrases_rev"

# Discover every currently supported public slot name.
slots = OpenCC.available_slots()
assert "HKPhrasesRev" in slots
assert "JPSCharactersRev" in slots

For advanced API use, parse_custom_dict_spec() reuses the same parser for the complete CLI-style slot:mode:path token. This avoids manually constructing CustomDictSpec records while keeping token parsing in the utility layer:

from opencc_purepy import OpenCC
from opencc_purepy.utils import parse_custom_dict_spec

tokens = [
    "hk_phrases_rev:append:./my_hk_dict.txt",
]

cc = OpenCC.from_dict_files(
    config="hk2sp",
    specs=[parse_custom_dict_spec(token) for token in tokens],
)

Use DictSlot.parse() when only a slot name needs normalization. Use parse_custom_dict_spec() when the input is the combined slot:mode:path form used by the CLI.

CLI custom dictionary files

The convert, office, and dictgen subcommands support -D/--custom-dict <slot:mode:path>. The option can be used multiple times. For convert and office, it follows the same packaged-JSON-plus-custom-files behavior as OpenCC.from_dict_files(). Specifications are applied from left to right; when multiple files affect the same slot, later specifications see and may replace entries produced by earlier ones.

opencc-purepy convert -c hk2sp \
  -D hkphrasesrev:append:./my_hk_dict.txt

Slot names accept enum-style names, compact names, and legacy keys, including HKPhrasesRev, hkphrasesrev, and hk_phrases_rev.

opencc-purepy office -i book.epub -c hk2sp \
  --custom-dict hkphrasesrev:append:./my_hk_dict.txt

For dictgen, -D/--custom-dict applies custom dictionary files before writing the generated JSON:

opencc-purepy dictgen -d ./my_dicts -o dictionary_maxlength.json \
  -D STPhrases:append:./UserDict.txt

# Compact JSON while preserving dictionary insertion/source order
opencc-purepy dictgen -o dictionary_maxlength.json --compact --no-sort

Post-load file customization

Use DictionaryMaxlength.with_custom_dict_files() when you already have a dictionary instance and want to apply OpenCC-compatible text dictionary files after loading it. Post-load customization supports both appends={...} and overrides={...}.

from opencc_purepy import DictSlot, OpenCC
from opencc_purepy.dictionary_lib import DictionaryMaxlength

dictionary = DictionaryMaxlength.from_json().with_custom_dict_files(
    appends={
        DictSlot.STPhrases: "./UserDict.txt",
        DictSlot.TWVariantsPhrases: "./custom/TWVariantsPhrases.txt",
        DictSlot.HKVariantsPhrases: "./custom/HKVariantsPhrases.txt",
    },
)

cc = OpenCC(config="s2t", dictionary=dictionary)

print(cc.convert("帕兰蒂尔是一家公司"))

Create a private dictionary instance first with DictionaryMaxlength.from_json() or DictionaryMaxlength.from_dicts(). Do not mutate the shared global provider returned by DictionaryMaxlength.get_provider() or DictionaryMaxlength.new(); the post-load customization APIs are intended for private dictionary instances.

Tofu-risk / Extension Unicode fallback pairs

Use DictionaryMaxlength.with_custom_dicts() for exact in-memory custom pairs when you need to patch tofu-risk characters or Extension Unicode mappings without restructuring the built-in OpenCC dictionaries.

This is useful for platforms where some CJK Extension characters may render as tofu boxes, or where you want to provide a temporary project-local fallback before the upstream dictionary data is updated.

from opencc_purepy import DictSlot, OpenCC
from opencc_purepy.dictionary_lib import DictionaryMaxlength

dictionary = DictionaryMaxlength.from_json().with_custom_dicts(
    appends={
        DictSlot.STPhrases: {
            # Project-local fallback pairs for tofu-risk / Extension Unicode cases.
            # Keep these patches small, explicit, and easy to remove later.
            "骖𬴂": "驂騑",
            "𫜩合": "齧合",
            "𫜩蘗吞针": "齧蘗吞針",

            # Normal custom phrase pairs may be mixed in as well.
            "帕兰蒂尔": "帕蘭蒂爾",
        },
        DictSlot.TWVariantsPhrases: {
            "喫茶小舖": "喫茶小舖",
        },
        DictSlot.HKVariantsPhrases: {
            "喫茶小舖": "喫茶小舖",
        },
    },
)

cc = OpenCC(config="s2t", dictionary=dictionary)

print(cc.convert("骖𬴂"))
print(cc.convert("𫜩合"))
print(cc.convert("帕兰蒂尔"))

This keeps the core dictionary structure unchanged while still allowing applications to patch specific high-risk entries at load time.

Dictionary text format

Custom dictionary files are UTF-8 text files in OpenCC lexicon format. Use one mapping per line:

# Custom company terms
帕兰蒂尔	帕蘭蒂爾
AI模型 AI模型

Each entry is parsed as key<TAB>value or key whitespace value. Blank lines are ignored, comments are allowed with #, and duplicate keys use late-comer wins.

Because file parsing follows OpenCC dictionary rules, leading spaces and embedded spaces in keys are not preserved. Use with_custom_dicts() when the custom key itself contains spaces.

Override mode

Use overrides={...} to replace an entire dictionary slot. This is for advanced users who maintain a full replacement for a selected OpenCC dictionary slot.

from opencc_purepy import DictSlot, OpenCC

cc = OpenCC.from_dicts(
    config="s2t",
    overrides={
        DictSlot.STPhrases: "./company/STPhrases.txt",
    },
)

Post-load override mode works the same way:

from opencc_purepy import DictSlot
from opencc_purepy.dictionary_lib import DictionaryMaxlength

dictionary = DictionaryMaxlength.from_json().with_custom_dict_files(
    overrides={
        DictSlot.STPhrases: "./CompanyOnlySTPhrases.txt",
    },
)

Supported slots

Forward regional variant phrase slots are applied before their character-level variant slots:

Japanese Shinjitai slots follow the upstream OpenCC JP layout:

Generate JSON with dictgen

TXT dictionaries are human-editable source files. dictionary_maxlength.json is a generated/cache format, so prefer dictgen instead of manually editing JSON.

opencc-purepy dictgen -d ./my_dicts -o dictionary_maxlength.json

# Optionally bake custom dictionary files into the generated JSON
opencc-purepy dictgen -d ./my_dicts -o dictionary_maxlength.json \
  -D STPhrases:append:./UserDict.txt

from opencc_purepy import OpenCC
from opencc_purepy.dictionary_lib import DictionaryMaxlength

dictionary = DictionaryMaxlength.from_json("opencc_purepy/dicts/dictionary_maxlength.json")

cc = OpenCC(
    config="s2t",
    dictionary=dictionary,
)

Which mode should I use?

• Use OpenCC.from_dict_files() or CLI -D/--custom-dict slot:mode:path when you want packaged JSON dictionaries plus a few custom files.
• Use OpenCC.from_dicts() when you want to load raw OpenCC TXT dictionary files or a custom TXT dictionary directory.
• Use overrides when maintaining a full proprietary replacement of an OpenCC dictionary file.
• Use with_custom_dict_files() to apply OpenCC-compatible text files to a private dictionary after loading it.
• Use with_custom_dicts() for exact in-memory pairs, especially keys with leading or embedded spaces.
• Use dictgen when you want to bake TXT dictionaries into JSON for reuse or faster loading.
• Use direct dictionary injection when sharing one loaded dictionary across many OpenCC instances.
• Prefer DictSlot for new code and IDE-friendly type checking.
• Legacy str slot keys remain fully supported for backward compatibility.

Unicode compatibility normalization

opencc_purepy provides optional Unicode normalization helpers for text that contains CJK compatibility ideographs, legacy glyph forms, radicals, or other mapped allographs. These helpers are separate from normal OpenCC conversion: they normalize Unicode forms without selecting a Simplified, Traditional, Taiwan, Hong Kong, or Japanese conversion configuration.

Three APIs are available:

• normalize_compat(text) — normalize CJK Compatibility Ideographs.
• normalize_unicode_compat(text) — normalize additional Unicode CJK compatibility and allograph mappings.
• normalize_compat_extended(text) — apply both normalization tables for the broadest normalization.

CJK Compatibility Ideographs

Use normalize_compat() when the input contains characters from the Unicode CJK compatibility ranges:

from opencc_purepy import OpenCC

cc = OpenCC("t2s")

text = "天龍八部書裡的喬峰是契丹人"
normalized = cc.normalize_compat(text)

print(normalized)
# 天龍八部書裡的喬峰是契丹人

# This pass only performs compatibility normalization. It does not automatically run OpenCC conversion:

converted = cc.convert(normalized)

print(converted)
# 天龙八部书里的乔峰是契丹人

Extended Unicode compatibility mappings

Use normalize_unicode_compat() for additional mapped CJK allographs and legacy forms outside the dedicated CJK Compatibility Ideographs table:

from opencc_purepy import OpenCC

cc = OpenCC("t2s")

text = "聼聼竒羙甁噐"
normalized = cc.normalize_unicode_compat(text)

print(normalized)
# 聽聽奇美瓶器

Unmapped characters are preserved unchanged.

Combined normalization

For text from PDFs, OCR, legacy documents, historical sources, or mixed Unicode data, normalize_compat_extended() is the convenient-combined pass. It applies the extended Unicode compatibility mappings and CJK Compatibility Ideographs normalization in the intended order:

from opencc_purepy import OpenCC

cc = OpenCC("t2s")

text = "聼聼竒羙⽟䂖甁噐⾳"

normalized = cc.normalize_compat_extended(text)
print(normalized)
# 聽聽奇美玉石瓶器音

converted = cc.convert(normalized)
print(converted)
# 听听奇美玉石瓶器音

Normalization is intentionally explicit and is not automatically performed by convert(). This keeps normal OpenCC conversion behavior unchanged and lets applications choose the preprocessing appropriate for their input.

When both Unicode normalization and DeToFu are needed, use the following processing order:

from opencc_purepy import OpenCC

text = "聼聼竒羙⽟䂖甁噐⾳"

cc = OpenCC("t2s")

text = cc.normalize_compat_extended(text)
text = cc.convert(text)
text = cc.detofu(text, "all")

In short:

Unicode normalization -> OpenCC conversion -> DeToFu

DeTofu display compatibility fallback

DeTofu replaces mapped tofu-risk rare CJK extension characters with display-compatible fallback characters. It is useful on systems, browsers, e-book readers, document viewers, or mobile platforms where some non-BMP CJK extension characters may render as tofu boxes, missing glyphs, or empty boxes.

DeTofu is a display-compatibility pass, not normal OpenCC dictionary conversion. It does not modify phrase matching, regional variant selection, script detection, or punctuation conversion. If you use it with converted text, apply it after convert(...).

from opencc_purepy import OpenCC
from opencc_purepy.detofu import DeTofuLevel

cc = OpenCC("t2s")

text = "儼驂騑於上路，訪風景於崇阿"
converted = cc.convert(text)

display_safe = cc.detofu(converted, DeTofuLevel.ExtB)

print(display_safe)

# String level names are also supported:

display_safe = cc.detofu(converted, "all")

DeTofu levels

DeTofu levels are threshold-based:

ExtB means ExtB and above
ExtC means ExtC and above
ExtD means ExtD and above
...
ExtI means ExtI only

Supported level names:

all
ext-b / b
ext-c / c
ext-d / d
ext-e / e
ext-f / f
ext-g / g
ext-h / h
ext-i / i

"all" is equivalent to ExtB.

Custom fallback file

Use detofu_with_custom_file(...) to add project-local fallback mappings from a UTF-8 text file:

from opencc_purepy import OpenCC

cc = OpenCC("t2s")

text = "儼驂騑於上路，訪風景於崇阿𱁬"
converted = cc.convert(text)

display_safe = cc.detofu_with_custom_file(
    converted,
    "all",
    "custom_tofu.txt",
)

File format:

tofu_char<TAB>fallback_char<TAB>extension

Example:

𱁬	?	ExtB

Blank lines are ignored, and lines beginning with # are ignored. The extension column accepts B through I or ExtB through ExtI. Custom file mappings override built-in mappings for the same tofu-risk character.

Custom in-memory pairs

Use detofu_with_custom_pairs(...) to add direct fallback pairs in memory:

from opencc_purepy import OpenCC

cc = OpenCC("t2s")

text = "儼驂騑於上路，訪風景於崇阿𱁬"
converted = cc.convert(text)

display_safe = cc.detofu_with_custom_pairs(
    converted,
    "all",
    {
        "𱁬": "?",
    },
)

Direct pairs do not have an extension column. They are always added to the selected map, and custom pairs override built-in mappings for the same tofu-risk character. Only the first Unicode scalar from each key and value is used. Empty keys or values are ignored.

DeTofu contract

• Unknown characters are preserved unchanged.
• DeTofu never replaces unknown characters with ?, □, �, or empty text.
• DeTofu only replaces characters that exist in the built-in or custom fallback map.
• DeTofu is intended as a final display-compatibility pass.
• For converted text, call cc.detofu(...) after cc.convert(...).

🧩 API Reference

Exports

• OpenCC
• OpenccConfig
• DictSlot
• DictSlotLike
• DeTofuLevel is available from opencc_purepy.detofu
• CustomDictSpec, parse_custom_dict_spec, parse_custom_dict_specs, and custom_dict_specs_to_maps are available from opencc_purepy.utils

OpenCC class

• OpenCC(config: Optional[Union[str, OpenccConfig]] = None, dictionary: Optional[DictionaryMaxlength] = None) Create a converter with a supported config string or OpenccConfig enum value. The config defaults to "s2t" when omitted. Advanced callers may inject a private or preloaded DictionaryMaxlength instance. Raises ValueError for unsupported configs.

• OpenCC.from_dicts(config="s2t", base_dir=None, paths=None, overrides=None, appends=None) -> OpenCC
  Create a converter by loading raw OpenCC TXT dictionary files through DictionaryMaxlength.from_dicts().

• OpenCC.from_dict_files(config="s2t", specs=None) -> OpenCC
  Create a converter from packaged JSON dictionaries, then apply CustomDictSpec(slot, mode, path) custom file specs.

• set_config(config: Union[str, OpenccConfig]) -> None
  Update the active conversion config. Raises ValueError for unsupported configs.

• get_config() -> str
  Return the current canonical config name.

• supported_configs() -> List[str]
  Return all supported config names.

• available_slots() -> List[str]
  Return the canonical names of all currently available dictionary slots.

• get_last_error() -> Optional[str]
  Return the last validation or conversion error, if any.

• convert(input_text: str, punctuation: bool = False) -> str
  Convert text using the active config, with optional punctuation conversion.

• s2t(input_text: str, punctuation: bool = False) -> str
  Simplified Chinese to Traditional Chinese.

• t2s(input_text: str, punctuation: bool = False) -> str
  Traditional Chinese to Simplified Chinese.

• s2tw(input_text: str, punctuation: bool = False) -> str
  Simplified Chinese to Taiwan Traditional.

• tw2s(input_text: str, punctuation: bool = False) -> str
  Taiwan Traditional to Simplified Chinese.

• s2twp(input_text: str, punctuation: bool = False) -> str
  Simplified Chinese to Taiwan Traditional with phrase and variant normalization.

• tw2sp(input_text: str, punctuation: bool = False) -> str
  Taiwan Traditional with idioms to Simplified Chinese.

• s2hk(input_text: str, punctuation: bool = False) -> str
  Simplified Chinese to Hong Kong Traditional.

• hk2s(input_text: str, punctuation: bool = False) -> str
  Hong Kong Traditional to Simplified Chinese.

• s2hkp(input_text: str, punctuation: bool = False) -> str
  Simplified Chinese to Hong Kong Traditional with phrase and variant normalization.

• hk2sp(input_text: str, punctuation: bool = False) -> str
  Hong Kong Traditional with phrases/variants to Simplified Chinese.

• t2tw(input_text: str, punctuation: bool = False) -> str
  Traditional Chinese to Taiwan Traditional.

• t2twp(input_text: str, punctuation: bool = False) -> str
  Traditional Chinese to Taiwan Traditional with phrase mappings.

• tw2t(input_text: str, punctuation: bool = False) -> str
  Taiwan Traditional to standard Traditional Chinese.

• tw2tp(input_text: str, punctuation: bool = False) -> str
  Taiwan Traditional to standard Traditional Chinese with phrase reversal.

• t2hk(input_text: str, punctuation: bool = False) -> str
  Traditional Chinese to Hong Kong variant.

• t2hkp(input_text: str, punctuation: bool = False) -> str Traditional Chinese to Hong Kong Traditional with phrase and variant normalization.

• hk2t(input_text: str, punctuation: bool = False) -> str
  Hong Kong Traditional to standard Traditional Chinese.

• hk2tp(input_text: str, punctuation: bool = False) -> str Hong Kong Traditional to standard Traditional Chinese with phrase and variant reversal.

• t2jp(input_text: str, punctuation: bool = False) -> str
  Traditional Chinese to Japanese variants.

• jp2t(input_text: str, punctuation: bool = False) -> str
  Japanese Shinjitai to Traditional Chinese.

• st(input_text: str) -> str
  Character-only Simplified to Traditional conversion.

• ts(input_text: str) -> str
  Character-only Traditional to Simplified conversion.

• zho_check(input_text: str) -> int
  Detect the input text type:
    1 - Traditional, 2 - Simplified, 0 - Others

• normalize_compat(text: Optional[str]) -> str
  Normalize mapped CJK Compatibility Ideographs while preserving unmapped characters.

• normalize_unicode_compat(text: Optional[str]) -> str
  Normalize additional mapped Unicode CJK compatibility forms and allographs.

• normalize_compat_extended(text: Optional[str]) -> str
  Apply extended Unicode compatibility normalization followed by CJK Compatibility Ideographs normalization.

• detofu( text: Optional[str], level: Union[DeTofuLevel, str] = DeTofuLevel.ExtB ) -> str

  Apply built-in DeTofu display-compatible fallback mappings.

• detofu_with_custom_file( text: Optional[str], level: Union[DeTofuLevel, str], path: str ) -> str

  Apply built-in DeTofu mappings plus a UTF-8 custom fallback file.

• detofu_with_custom_pairs( text: Optional[str], level: Union[DeTofuLevel, str], pairs: Union[ Mapping[str, str], Iterable[Tuple[str, str]] ] ) -> str

  Apply built-in DeTofu mappings plus direct in-memory fallback pairs.

DictSlot enum

• parse(value: Union[DictSlot, str]) -> DictSlot
  Normalize an enum member, enum-style name, compact name, or legacy underscore-separated key.
• canonical_name() -> str
  Return the public enum-style slot name, such as "HKPhrasesRev".

OpenccConfig enum

• Members include: S2T, T2S, S2TW, TW2S, S2TWP, TW2SP, S2HK, HK2S, S2HKP, HK2SP, T2TW, TW2T, T2TWP, TW2TP, T2HK, HK2T, T2HKP, HK2TP, T2JP, JP2T
• to_canonical_name() -> str
  Return the lowercase OpenCC config string.
• parse(value: str) -> OpenccConfig
  Parse a config string into an enum value.

🛠 Development

• Python bindings: opencc_purepy/__init__.py, opencc_purepy/core.py
• CLI: opencc_purepy/__main__.py

⚡ Benchmark

OpenCC benchmark summary

Measured on GitHub Actions ubuntu-latest using the default s2t configuration. Benchmark separates cold startup, first post-init conversion, and warm cached conversion.

Runner Platform

Results

opencc-purepy v1.4.1

cold_total includes OpenCC(config) setup plus conversion. post_init_cold measures the first conversion after initialization. warm measures conversion after the union cache has already been built. Results depend on runner hardware and background system load.

Notes

Despite being implemented in pure Python, opencc_purepy achieves competitive conversion throughput through aggressive caching and starter-index optimizations.

The warm conversion path is practical for large-text workloads such as document conversion, GUI applications, and batch processing.

Projects That Use opencc-purepy

OpenccPurepyGui

📄 License

This project is licensed under the MIT License.

Powered by Pure Python and OpenCC Lexicons.