opencc-py 1.4.0

Conversion between Traditional and Simplified Chinese (pure Python)

opencc-py (OpenCC Pure Python Implementation)

This directory contains a pure Python implementation of the OpenCC Chinese conversion algorithm. It provides the same import surface as the Python package:

import opencc

converter = opencc.OpenCC("s2t")
print(converter.convert("汉字"))  # 漢字

Data Dependency

The package does not bundle OpenCC configs or dictionaries directly. Built-in conversion data is loaded from the opencc-data PyPI package at runtime.

This keeps the pure Python package small and avoids depending on generated files under the OpenCC source tree. The converter reads:

• config JSON files from opencc_data.config_path()
• dictionary text files from opencc_data.data_path()
• test cases from opencc_data.test_data_path()

Custom config files are still supported. When a custom config references a local dictionary path such as CustomPhrases.ocd2, the pure Python implementation looks for the corresponding CustomPhrases.txt next to the config file.

Installation

The PyPI package name is opencc-py. Users can install it with pip:

python -m pip install opencc-py

For local development from this directory:

python -m pip install .

The package version matches its opencc-data version and declares the matching data package as an exact install dependency, so pip installs the compatible data package automatically.

Or use editable development mode:

python -m pip install -e .

Supported Configs

opencc.CONFIGS is populated from the configs exposed by opencc-data.

import opencc

print(opencc.CONFIGS)

The standard mmseg configs and configs that do not require segmentation are supported. Jieba plugin configs are not included in opencc-data, so they are not exposed as built-in configs by this package.

Testing

Install test dependencies, then run pytest from the repository root:

python -m pip install -r python-pure/tests/requirements_lock.txt
PYTHONPATH=python-pure python -m pytest python-pure/tests

The tests verify:

• importing and initializing every built-in config
• conversion against opencc-data test cases
• custom config and local dictionary resolution
• golden output compatibility for supported configs

OpenCC 1.3.2 Feature Coverage

The following OpenCC 1.3.2 features are fully supported:

• CJK Compatibility Ideographs normalization — all built-in configs include a pre-processing normalization step that maps U+F900–U+FAFF characters to their canonical code points before conversion.
• match_policy: union — dictionary groups with "match_policy": "union" return the globally longest match across all sub-dictionaries.
• normalization config field — custom configs may add a normalization array to apply conversion steps before segmentation.
• New configs — s2hkp and hk2sp (Simplified ↔ Hong Kong, with phrase conversion) are available through opencc-data.
• Tofu-risk dictionary suppression — pass include_tofu_risk_dictionaries=False to OpenCC() to exclude dictionaries that may produce characters absent from modern CJK fonts.
• JSONC — config files may use // line comments and /* */ block comments; the pure Python backend strips them before JSON parsing.
• Inline dictionaries — {"type": "inline", "entries": {"key": "value", ...}} dict nodes are supported in custom configs.

Differences from the Official Implementation

This package intentionally implements only the pieces needed for pure Python text conversion. Compared with the official C++ library and command-line tools, it omits several lower-level details. The official Python implementation is the opencc PyPI package.

• binary dictionary loading for .ocd2/.ocd; built-in dictionaries are read from .txt data supplied by opencc-data
• dictionary compilation and extraction tools such as opencc_dict and opencc_phrase_extract
• the C API, shared-library loading behavior, and ABI/plugin compatibility guarantees
• native CLI behavior, including streaming I/O, command-line option parity, and platform-specific path handling
• package, runfiles, and source-tree data discovery fallbacks; built-in data comes from opencc-data
• automatic loading of optional plugin configs or plugin resources, including the Jieba plugin package layout
• performance optimizations from marisa-trie, Darts, and the C++ segmentation implementation

The conversion semantics still mirror OpenCC's config-driven pipeline: mmseg segmentation, ordered dictionary groups, longest-prefix matching within a dictionary, conversion chains, normalization, and optional suppression of tofu-risk dictionaries.

License and Compliance

This package is distributed under the Apache License 2.0.

This project is a derivative work of OpenCC. Runtime conversion data is provided by the opencc-data PyPI package.

---

opencc-py (OpenCC 純 Python 實作)

此目錄包含 OpenCC 中文轉換演算法的純 Python 實作，提供與 Python package 相同的匯入介面：

import opencc

converter = opencc.OpenCC("s2t")
print(converter.convert("汉字"))  # 漢字

資料依賴

此 package 不再直接內嵌 OpenCC config 或 dictionary。內建轉換資料會在執行時 從 PyPI package opencc-data 載入。

這能讓 pure Python package 保持精簡，並避免依賴 OpenCC source tree 底下的 生成檔案。converter 會讀取：

• opencc_data.config_path() 提供的 config JSON 檔案
• opencc_data.data_path() 提供的 dictionary text 檔案
• opencc_data.test_data_path() 提供的測試案例

自訂 config 仍然支援。當自訂 config 參照本地 dictionary 路徑，例如 CustomPhrases.ocd2，純 Python 實作會在 config 檔案旁尋找對應的 CustomPhrases.txt。

安裝

PyPI package 名稱是 opencc-py。使用者可以透過 pip 安裝：

python -m pip install opencc-py

從此目錄進行本地開發安裝：

python -m pip install .

此 package 的版本會與 opencc-data 版本一致，並將相同版本的資料 package 宣告為精確安裝依賴，因此 pip 會自動安裝相容的資料 package。

也可以使用 editable development mode：

python -m pip install -e .

支援的 Configs

opencc.CONFIGS 由 opencc-data 提供的 configs 產生。

import opencc

print(opencc.CONFIGS)

標準 mmseg configs 與不需要 segmentation 的 configs 皆受支援。Jieba plugin configs 不包含在 opencc-data 中，因此此 package 不會把它們列為內建 configs。

測試

先安裝測試依賴，再從 repository root 執行 pytest：

python -m pip install -r python-pure/tests/requirements_lock.txt
PYTHONPATH=python-pure python -m pytest python-pure/tests

測試會驗證：

• 每個內建 config 都能 import 與初始化
• 轉換結果符合 opencc-data 測試案例
• 自訂 config 與本地 dictionary 解析
• 支援 configs 的 golden output 相容性

OpenCC 1.3.2 功能支援狀況

以下 OpenCC 1.3.2 功能已完整支援：

• CJK 相容表意文字正規化 — 所有內建 config 均包含正規化前處理步驟， 在轉換前先將 U+F900–U+FAFF 區塊字元映射至標準碼位。
• match_policy: union — 使用 "match_policy": "union" 的 dictionary group 會取所有子 dictionary 中最長的前綴命中。
• normalization config 欄位 — 自訂 config 可加入 normalization 陣列， 在 segmentation 前插入正規化步驟。
• 新 configs — s2hkp 與 hk2sp（簡體 ↔ 香港繁體，含詞組轉換） 透過 opencc-data 提供。
• Tofu-risk dictionary 停用 — 建構 OpenCC() 時傳入 include_tofu_risk_dictionaries=False 可停用可能輸出現代字型缺字的 dictionary。
• JSONC — config 檔案支援 // 行注釋與 /* */ 區塊注釋；純 Python 後端在解析 JSON 前會先剝除注釋。
• Inline dictionary — 自訂 config 支援 {"type": "inline", "entries": {"key": "value", ...}} 節點。

與官方實作的差異

此 package 刻意只實作純 Python 文字轉換所需的部分。相較於官方 C++ library 與 command-line tools，它省略了幾個較底層的實作細節。官方 Python 實作是 PyPI 上的 opencc package。

• .ocd2 / .ocd 二進位 dictionary 載入；內建 dictionary 會讀取 opencc-data 提供的 .txt 資料
• opencc_dict、opencc_phrase_extract 等 dictionary 編譯與抽取工具
• C API、shared-library 載入行為，以及 ABI/plugin 相容性保證
• native CLI 行為，包括 streaming I/O、命令列選項完整對齊，以及平台相關路徑處理
• package、runfiles、source-tree 資料搜尋 fallback；內建資料一律來自 opencc-data
• optional plugin configs 或 plugin resources 的自動載入，包括 Jieba plugin 的 package layout
• marisa-trie、Darts 與 C++ segmentation 實作帶來的效能最佳化

轉換語意仍會對齊 OpenCC 的 config-driven pipeline：mmseg segmentation、 ordered dictionary groups、dictionary 內 longest-prefix matching、conversion chains、normalization，以及 tofu-risk dictionaries 的可選停用。

License 與合規

此 package 以 Apache License 2.0 發佈。

此專案屬於 OpenCC 的衍生作品。執行時轉換 資料由 PyPI package opencc-data 提供。