opencc-pyo3 0.9.0

High-performance Chinese text conversion (Simplified ↔ Traditional) powered by Rust, PyO3, and OpenCC lexicons.

opencc_pyo3

opencc_pyo3 is a Python extension module powered by Rust and PyO3, providing fast and accurate conversion between different Chinese text variants using OpenCC algorithms.

Features

• Convert between Simplified, Traditional, Hong Kong, Taiwan, and Japanese Kanji variants with OpenCC-compatible configurations.
• High-performance Rust + PyO3 backend for fast, memory-efficient Chinese text conversion in Python.
• Python API with OpenCC, OpenccConfig, config validation helpers, punctuation conversion, and Chinese text variant detection.
• Command-line interface for plain text conversion from files or standard input.
• Office and EPUB document conversion support for .docx, .xlsx, .pptx, .odt, .ods, .odp, and .epub.
• PDF text extraction helpers, including PDFium-based page-by-page extraction utilities.
• CJK paragraph reflow helper for cleaning PDF-extracted text before conversion.

Supported Conversion Configurations

• s2t, t2s, s2tw, tw2s, s2twp, tw2sp, s2hk, hk2s, t2tw, tw2t, t2twp, tw2tp, t2hk, hk2t, t2jp, jp2t

Installation

1. Install from PyPI

pip install opencc-pyo3

2. Build and install the Python wheel using maturin:

# In project root
maturin build --release
pip install ./target/wheels/opencc_pyo3-<version>-cp<pyver>-abi3-<platform>.whl

Or for development (May require venv):

maturin develop -r

See build.txt for detailed build and install instructions.

Usage

Python

from opencc_pyo3 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。」

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CLI

You can also use the CLI interface via Python module or Python script:
Sub-Commands are:

• convert: Convert Chinese text using OpenCC
• office: Convert Office document Chinese text using OpenCC
• pdf: Convert extracted PDF document text using OpenCC

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convert

python -m opencc_pyo3 convert --help
usage: opencc-pyo3 convert [-h] [-i <file>] [-o <file>] [-c <conversion>] [-p] [--in-enc <encoding>] [--out-enc <encoding>]

options:
  -h, --help            show this help message and exit
  -i, --input <file>    Read original text from <file>.
  -o, --output <file>   Write converted text to <file>.
  -c, --config <conversion>
                        Conversion configuration: s2t|s2tw|s2twp|s2hk|t2s|tw2s|tw2sp|hk2s|jp2t|t2jp
  -p, --punct           Enable punctuation conversion. (Default: False)
  --in-enc <encoding>   Encoding for input. (Default: UTF-8)
  --out-enc <encoding>  Encoding for output. (Default: UTF-8)

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office

Support OpenOffice documents and Epub (.docx, .xlsx, .pptx, .odt, .ods, .odp, .epub)

python -m opencc_pyo3 office --help                                         
usage: opencc-pyo3 office [-h] [-i <file>] [-o <file>] [-c <conversion>] [-p] [-f <format>] [--auto-ext] [--keep-font]

options:
  -h, --help            show this help message and exit
  -i, --input <file>    Input Office document from <file>.
  -o, --output <file>   Output Office document to <file>.
  -c, --config <conversion>
                        conversion: s2t|s2tw|s2twp|s2hk|t2s|tw2s|tw2sp|hk2s|jp2t|t2jp
  -p, --punct           Enable punctuation conversion. (Default: False)
  -f, --format <format>
                        Target Office format (e.g., docx, xlsx, pptx, odt, ods, odp, epub)
  --auto-ext            Auto-append extension to output file
  --keep-font           Preserve font-family information in Office content

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PDF

Support PDF files as input, with built-in text extraction and OpenCC-based conversion powered by opencc-fmmseg (available since v0.8.4).

This command allows you to extract Chinese text from PDF documents, optionally apply CJK-aware paragraph reflow, and convert the result using OpenCC configurations.

Note
Only text-embedded (searchable) PDF documents are supported.
Scanned or image-only PDFs without an embedded text layer are not currently supported.

python -m opencc_pyo3 pdf --help

usage: __main__.py pdf [-h] -i <file> [-o <file>] [-c <conversion>] [-p] [-H] [-r] [--compact] [--timing] [-e]

options:
  -h, --help            show this help message and exit
  -i, --input <file>    Input PDF file.
  -o, --output <file>   Output text file (UTF-8). If omitted, defaults to "<input>_converted.txt".
  -c, --config <conversion>
                        Conversion configuration: s2t|s2tw|s2twp|s2hk|t2s|tw2s|tw2sp|hk2s|jp2t|t2jp
  -p, --punct           Enable punctuation conversion. (Default: False)
  -H, --header          Preserve page-break-like gaps when reflowing CJK paragraphs (passed as add_pdf_page_header to reflow_cjk_paragraphs).
  -r, --reflow          Enable CJK-aware paragraph reflow before conversion.
  --compact             Use compact paragraph mode (single newline between paragraphs).
  --timing              Show time use for each process workflow.
  -e, --extract         Extract PDF text only (skip OpenCC conversion).

python -m opencc_pyo3 convert -i input.txt -o output.txt -c s2t --punct

python -m opencc_pyo3 office -c s2t --punct -i input.docx -o output.docx --keep-font

opencc-pyo3 office -c s2tw -p -i input.epub -o output.epub

opencc-pyo3 pdf -i input.pdf -o output.txt -c s2t -punct --reflow

---

Python API

OpenccConfig

OpenccConfig is an enum exported from opencc_pyo3 for config-safe usage:

from opencc_pyo3 import OpenCC, OpenccConfig

cc = OpenCC(OpenccConfig.S2TW)
print(cc.convert("汉字"))  # 漢字

Available enum values:

• OpenccConfig.S2T
• OpenccConfig.T2S
• OpenccConfig.S2TW
• OpenccConfig.TW2S
• OpenccConfig.S2TWP
• OpenccConfig.TW2SP
• OpenccConfig.S2HK
• OpenccConfig.HK2S
• OpenccConfig.T2TW
• OpenccConfig.TW2T
• OpenccConfig.T2TWP
• OpenccConfig.TW2TP
• OpenccConfig.T2HK
• OpenccConfig.HK2T
• OpenccConfig.T2JP
• OpenccConfig.JP2T

OpenCC

Core converter class backed by the Rust extension module.

• OpenCC(config: str | OpenccConfig = "s2t")
  • Creates a converter using a config string or OpenccConfig enum.
  • Invalid config values fall back to s2t.
• OpenCC.from_dicts(config="s2t", specs=None) -> OpenCC
  • Creates a converter with programmatic, in-memory custom dictionary entries.
• OpenCC.from_dict_files(config="s2t", specs=None) -> OpenCC
  • Creates a converter with OpenCC-style custom dictionary files.
• convert(input_text: str, punctuation: bool = False) -> str
  • Converts text using the current config.
• set_config(config: str | OpenccConfig) -> None
  • Changes the active config.
• get_config() -> str
  • Returns the current canonical config name.
• get_last_error() -> str
  • Returns the most recent config error message, or "" if none.
• zho_check(input_text: str) -> int
  • Detects the text type.
  • 1 = Traditional Chinese, 2 = Simplified Chinese, 0 = other / undetermined
• OpenCC.supported_configs() -> list[str]
  • Returns all supported config names.
• OpenCC.is_valid_config(config: str) -> bool
  • Validates a config string.

Example:

from opencc_pyo3 import OpenCC, OpenccConfig

cc = OpenCC(OpenccConfig.S2T)
print(cc.get_config())  # s2t
print(cc.convert("汉字", punctuation=True))
print(cc.zho_check("汉字"))  # 2

cc.set_config("t2jp")
print(cc.convert("圖書館"))  # 図書館

print(OpenCC.supported_configs())
print(OpenCC.is_valid_config("s2hk"))  # True

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Custom Dictionaries

OpenCC("s2t") remains the recommended API for normal use and continues to use the built-in embedded dictionaries. Use custom dictionaries only when you need project-specific terms or overrides.

Custom dictionaries are applied during construction. The backend first loads the default embedded zstd dictionaries, then applies post-load customization with DictionaryMaxlength::from_zstd()?.with_custom_dicts(...) or DictionaryMaxlength::from_zstd()?.with_custom_dict_files(...). The final OpenCC instance remains immutable and optimized after construction. Runtime hot reload is not supported; rebuild a new OpenCC instance if dictionaries need to change.

In-memory custom dictionaries

Use OpenCC.from_dicts() for programmatic terms:

from typing import List

from opencc_pyo3 import OpenCC, CustomDictSpec

specs: List[CustomDictSpec] = [
    {
        "slot": "STPhrases",
        "pairs": [("帕兰蒂尔", "柏蘭蒂爾")],
        "mode": "append",
    }
]

cc = OpenCC.from_dicts("s2t", specs)

print(cc.convert("帕兰蒂尔是一家公司"))
# 柏蘭蒂爾是一家公司

File-based custom dictionaries

Use OpenCC.from_dict_files() for OpenCC-style dictionary files:

from typing import List

from opencc_pyo3 import OpenCC, CustomDictFileSpec

specs: List[CustomDictFileSpec] = [
    {
        "slot": "STPhrases",
        "files": ["custom_st_phrases.txt"],
        "mode": "append",
    }
]

cc = OpenCC.from_dict_files("s2t", specs)

Custom dictionary files use one mapping per line:

source<TAB>target

Example:

帕兰蒂尔	柏蘭蒂爾

Merge modes

• append: add custom entries to the existing dictionary slot. Duplicate keys follow the backend "last wins" behavior.
• override: replace the entire target dictionary slot with the custom entries or files.

Supported dictionary slots

Use canonical slot names without .txt, such as STPhrases, not STPhrases.txt. The Python wrapper may tolerate .txt, but the documented API uses canonical names only.

Slot	Purpose	Original OpenCC file
STCharacters	Simplified → Traditional character mappings	STCharacters.txt
STPhrases	Simplified → Traditional phrase mappings	STPhrases.txt
STPunctuations	Simplified → Traditional punctuation mappings	STPunctuations.txt
TSCharacters	Traditional → Simplified character mappings	TSCharacters.txt
TSPhrases	Traditional → Simplified phrase mappings	TSPhrases.txt
TSPunctuations	Traditional → Simplified punctuation mappings	TSPunctuations.txt
TWPhrases	Traditional → Taiwan phrase mappings	TWPhrases.txt
TWPhrasesRev	Taiwan → Traditional reverse phrase mappings	TWPhrasesRev.txt
TWVariants	Traditional → Taiwan regional variant mappings	TWVariants.txt
TWVariantsRev	Taiwan → Traditional reverse variant mappings	TWVariantsRev.txt
TWVariantsRevPhrases	Taiwan → Traditional reverse phrase variant mappings	TWVariantsRevPhrases.txt
HKVariants	Traditional → Hong Kong regional variant mappings	HKVariants.txt
HKVariantsRev	Hong Kong → Traditional reverse variant mappings	HKVariantsRev.txt
HKVariantsRevPhrases	Hong Kong → Traditional reverse phrase variant mappings	HKVariantsRevPhrases.txt
JPSCharacters	Japanese Shinjitai character mappings	JPShinjitaiCharacters.txt
JPSPhrases	Japanese Shinjitai phrase mappings	JPShinjitaiPhrases.txt
JPVariants	Traditional → Japanese variant mappings	JPVariants.txt
JPVariantsRev	Japanese → Traditional reverse variant mappings	JPVariantsRev.txt

Custom dictionary behavior follows the same OpenCC dictionary-slot model. Choosing the wrong slot may have no effect or may affect a different conversion path. For s2t, use STCharacters or STPhrases. For t2s, use TSCharacters or TSPhrases. For regional variants, use the relevant TW, HK, or JP slots.

Typing helpers

CustomDictSpec and CustomDictFileSpec are exported for typed Python code:

from typing import List
from opencc_pyo3 import OpenCC, CustomDictSpec

specs: List[CustomDictSpec] = [
    {
        "slot": "STPhrases",
        "pairs": [("帕兰蒂尔", "柏蘭蒂爾")],
        "mode": "append",
    }
]

cc = OpenCC.from_dicts("s2t", specs)

Notes:

• Custom dictionaries are loaded at construction time.
• Existing OpenCC objects are immutable after construction.
• Runtime hot reload is not supported.
• Rebuild a new OpenCC instance if dictionaries need to change.
• Invalid slots, invalid modes, malformed lines, or unreadable files raise errors.
• OpenCC("s2t") remains the recommended API for normal users.
• Use from_dicts() for programmatic or in-memory custom terms.
• Use from_dict_files() for OpenCC-style dictionary files.

---

Reflow helper

• reflow_cjk_paragraphs(text: str, add_pdf_page_header: bool, compact: bool) -> str
  • Reflows PDF-extracted CJK text by merging broken line wraps while preserving paragraph structure.
  • add_pdf_page_header=True keeps explicit page-gap style boundaries.
  • compact=True uses single newlines between paragraphs.

Example:

from opencc_pyo3.opencc_pyo3 import reflow_cjk_paragraphs

raw_text = "我来到\n无人海边。"
clean_text = reflow_cjk_paragraphs(raw_text, add_pdf_page_header=False, compact=False)  # 我来到无人海边。

PDF extraction APIs

The package exposes PDFium-based PDF extraction helpers in opencc_pyo3.pdfium_helper.

Import from opencc_pyo3.pdfium_helper:

• extract_pdf_pages_with_callback_pdfium(path: str, callback, add_page_header: bool = False) -> None
• extract_pdf_text_pdfium_progress(path: str) -> str
• extract_pdf_text_pdfium_silent(path: str) -> str
• extract_pdf_text_pages_pdfium(path: str) -> list[str]
• extract_pdf_text_pages_pdfium_progress(path: str) -> list[str]
• make_progress_collector() -> tuple[callback, list[str]]
• make_silent_collector() -> tuple[callback, list[str]]

Example:

from opencc_pyo3 import OpenCC
from opencc_pyo3.opencc_pyo3 import reflow_cjk_paragraphs
from opencc_pyo3.pdfium_helper import extract_pdf_text_pdfium_silent

raw = extract_pdf_text_pdfium_silent("input.pdf")
text = reflow_cjk_paragraphs(raw, add_pdf_page_header=False, compact=False)
converted = OpenCC("s2t").convert(text, punctuation=True)

Development

• Rust source: src/lib.rs
• Python bindings: opencc_pyo3/__init __.py, opencc_pyo3/opencc_pyo3.pyi
• CLI: opencc_pyo3/main.py

Benchmarks

Latest benchmark results for the optimized current opencc_pyo3 version. These replace the much older v0.7.0 numbers.

Package: opencc_pyo3
Python: 3.13.13
Platform: Windows-11-10.0.26200-SP0
Processor: Intel64 Family 6 Model 191 Stepping 2, GenuineIntel
Configs: s2t, s2tw, s2twp
Text sizes: 100, 1,000, 10,000, 100,000 characters

BENCHMARK RESULTS

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Method	Config	TextSize	Mean	StdDev	Min	Max	Ops/sec	Chars/sec
Convert_Small	s2t	100	0.005 ms	0.003 ms	0.004 ms	0.021 ms	188,442	18,844,221
Convert_Medium	s2t	1,000	0.038 ms	0.006 ms	0.036 ms	0.066 ms	26,189	26,189,437
Convert_Large	s2t	10,000	0.253 ms	0.093 ms	0.171 ms	0.629 ms	3,958	39,577,314
Convert_XLarge	s2t	100,000	1.394 ms	0.166 ms	1.156 ms	1.699 ms	717	71,726,750
Convert_Small	s2tw	100	0.006 ms	0.003 ms	0.005 ms	0.021 ms	175,953	17,595,308
Convert_Medium	s2tw	1,000	0.044 ms	0.005 ms	0.042 ms	0.071 ms	22,808	22,808,485
Convert_Large	s2tw	10,000	0.318 ms	0.086 ms	0.227 ms	0.514 ms	3,141	31,411,310
Convert_XLarge	s2tw	100,000	1.503 ms	0.129 ms	1.355 ms	1.837 ms	665	66,516,340
Convert_Small	s2twp	100	0.008 ms	0.003 ms	0.007 ms	0.025 ms	130,435	13,043,478
Convert_Medium	s2twp	1,000	0.054 ms	0.006 ms	0.052 ms	0.084 ms	18,378	18,377,849
Convert_Large	s2twp	10,000	0.482 ms	0.249 ms	0.335 ms	1.602 ms	2,075	20,746,888
Convert_XLarge	s2twp	100,000	1.817 ms	0.197 ms	1.649 ms	2.581 ms	550	55,032,341

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Reproduce Benchmarks

python bench/opencc_benchmark_md.py --ci --configs s2t s2tw s2twp --sizes Small Medium Large XLarge --export md json --output-dir bench/out

Projects That Use opencc-pyo3

OpenccPyo3Gui

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License

MIT

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Powered by Rust, PyO3, OpenCC, Pdfium and opencc-fmmseg.