opencc-fmmseg

opencc-fmmseg is a high-performance Rust-based engine for Chinese text conversion.
It combines OpenCC's lexicons with an optimized Forward Maximum Matching (FMM) algorithm, suitable for:

• Traditional ↔ Simplified Chinese text conversion
• Lexicon-based segmentation
• CLI tools and system integration via C/C++ or Python bindings

🦀 Example (Rust)

use opencc_fmmseg::OpenCC;

fn main() {
    let input = "汉字转换测试";
    let opencc = OpenCC::new();
    let output = opencc.convert(input, "s2t", false);
    println!("{}", output);  // 漢字轉換測試
}

📦 Download

Grab the latest version for your platform from the Releases page:

Each archive contains:

README.txt
version.txt
bin/ # Command-line tools
lib/ # Shared library (.dll / .so / .dylib)
include/ # C API header + C++ helper header

Features

• 📦 Simple CLI tool for converting between Simplified and Traditional Chinese.
• 🔍 Lexicon-driven segmentation using OpenCC dictionaries.
• ⚡ High performance using parallel processing.
• 🛠️ Designed to be easily embedded as a Rust library or used standalone.

Installation

git clone https://github.com/laisuk/opencc-fmmseg
cd opencc-fmmseg
cargo build --release --workspace

🚀 CLI Usage

The CLI tool will be located at:

target/release/

opencc-rs          # CLI plain text and Office document text converter
opencc-clip        # Convert from clipboard, auto detect config
dict-generate      # Generate dictionary ZSTD, CBOR or JSON files

Usage

opencc-rs convert

Convert plain text using OpenCC

Usage: opencc-rs.exe convert [OPTIONS] --config <config>

Options:
  -i, --input <file>       Input file (use stdin if omitted for non-office documents)
  -o, --output <file>      Output file (use stdout if omitted for non-office documents)
  -c, --config <config>    Conversion configuration [possible values: s2t, t2s, s2tw, tw2s, s2twp, tw2sp, s2hk, hk2s, t2tw, t2twp, t2hk, tw2t, tw2tp, hk2t, t2jp, jp2t]
  -p, --punct              Enable punctuation conversion
      --in-enc <in_enc>    Encoding for input [default: UTF-8]
      --out-enc <out_enc>  Encoding for output [default: UTF-8]
  -h, --help               Print help

opencc-rs office

Convert Office or EPUB documents using OpenCC

Usage: opencc-rs.exe office [OPTIONS] --config <config>

Options:
  -i, --input <file>     Input file (use stdin if omitted for non-office documents)
  -o, --output <file>    Output file (use stdout if omitted for non-office documents)
  -c, --config <config>  Conversion configuration [possible values: s2t, t2s, s2tw, tw2s, s2twp, tw2sp, s2hk, hk2s, t2tw, t2twp, t2hk, tw2t, tw2tp, hk2t, t2jp, jp2t]
  -p, --punct            Enable punctuation conversion
  -f, --format <ext>     Force document format: docx, odt, epub...
      --keep-font        Preserve original font styles
      --auto-ext         Infer format from file extension
  -h, --help             Print help

Example

Plain Text

./opencc-rs convert -c s2t -i text_simplified.txt -o text_traditional.txt

Office Documents or EPUB

• Supported OpenDocument formats: .docx, .xlsx, .pptx, .odt, .ods, .odp, .epub

./opencc-rs office -c s2t --punct --format docx -i doc_simplified.docx -o doc_traditional.docx

• Supported conversions:
  • s2t – Simplified to Traditional
  • s2tw – Simplified to Traditional Taiwan
  • s2twp – Simplified to Traditional Taiwan with idioms
  • t2s – Traditional to Simplified
  • tw2s – Traditional Taiwan to Simplified
  • tw2sp – Traditional Taiwan to Simplified with idioms
  • etc

Lexicons

By default, it uses OpenCC's built-in lexicon paths. You can also provide your own lexicon dictionary generated by dict-generate CLI tool.

📚 Library Usage

You can also use opencc-fmmseg as a library:
To use opencc-fmmseg in your project, add this to your Cargo.toml:

[dependencies]
opencc-fmmseg = "0.8.0"  # or latest version

Then use it in your code:

use opencc_fmmseg::OpenCC;

fn main() {
    let input = "这是一个测试";
    let opencc = OpenCC::new();
    let output = opencc.convert(input, "s2t", false);
    println!("{}", output); // -> "這是一個測試"
}

📦 Crate: opencc-fmmseg on crates.io
📄 Docs: docs.rs/opencc-fmmseg

🧩 C/C++ Integration (opencc_fmmseg_capi)

You can also use opencc-fmmseg via a C API for integration with C/C++ projects.

The zip includes:

• {lib}opencc_fmmseg_capi.{so,dylib,dll}
• C API: opencc_fmmseg_capi.h
• Header-only C++ helper: OpenccFmmsegHelper.hpp

You can link against the shared library and call the segmentation/convert functions from any C or C++ project.

Example 1

#include "opencc_fmmseg_capi.h"
void* handle = opencc_new();
const char* config = "s2t";
const char* result = opencc_convert(handle, "汉字", config, false);
opencc_delete(handle);

Example 2

#include <stdio.h>
#include "opencc_fmmseg_capi.h"

int main(int argc, char **argv) {
    void *opencc = opencc_new();
    bool is_parallel = opencc_get_parallel(opencc);
    printf("OpenCC is_parallel: %d\n", is_parallel);

    const char *config = u8"s2twp";
    const char *text = u8"意大利邻国法兰西罗浮宫里收藏的“蒙娜丽莎的微笑”画像是旷世之作。";
    printf("Text: %s\n", text);

    int code = opencc_zho_check(opencc, text);
    printf("Text Code: %d\n", code);

    char *result = opencc_convert(opencc, text, config, true);
    code = opencc_zho_check(opencc, result);

    char *last_error = opencc_last_error();
    printf("Converted: %s\n", result);
    printf("Text Code: %d\n", code);
    printf("Last Error: %s\n", last_error == NULL ? "No error" : last_error);

    if (last_error != NULL) {
        opencc_error_free(last_error);
    }
    if (result != NULL) {
        opencc_string_free(result);
    }
    if (opencc != NULL) {
        opencc_delete(opencc);
    }

    return 0;
}

Output

OpenCC is_parallel: 1
Text: 意大利邻国法兰西罗浮宫里收藏的“蒙娜丽莎的微笑”画像是旷世之作。
Text Code: 2
Converted: 義大利鄰國法蘭西羅浮宮裡收藏的「蒙娜麗莎的微笑」畫像是曠世之作。
Text Code: 1
Last Error: No error

Notes

• opencc_new() initializes the engine.
• opencc_convert(...) performs the conversion with the specified config (e.g., s2t, t2s, s2twp).
• opencc_string_free(...) must be called to free the returned string.
• opencc_delete(...) must be called to free OpenCC object.
• opencc_zho_check(...) to detect zh-Hant (1), zh-Hans (2), others (0).
• Parallelism support can be queried using opencc_get_parallel().
• Errors are returned from opencc_last_error().

Project Structure

• src/lib.rs – Main library with segmentation logic.
• capi/opencc-fmmseg-capi C API source and demo.
• tools/opencc-rs/src/main.rs – CLI tool (opencc-rs) implementation.
• dicts/ – OpenCC text lexicons which converted into Zstd compressed CBOR format.

🛠 Built With

• Rust + Cargo Workspaces
• OpenCC-compatible dictionaries
• Parallelized FMM segmentation
• GitHub Actions cross-platform release automation

🚀 Benchmark Results: opencc-fmmseg Conversion Speed

Tested using Criterion.rs on 12,000-character text with punctuation disabled (punctuation = false).

Results from v0.8.0:

📊 Throughput Interpretation

• ~62–77 million characters per second
• ≈ 100 full-length novels (500k chars each) per second
• ≈ 1 GB of UTF-8 text processed in under 10 seconds

At this scale, performance is so high that I/O (disk or network), not the converter, becomes the bottleneck.

🏅 Highlights

Credits

• OpenCC by BYVoid Carbo Kuo – Lexicon source.

📜 License

• MIT License.
• © Laisuk Lai.
• See LICENSE for details.

💬 Feedback / Contributions

• Issues and pull requests are welcome.
• If you find this tool useful, please ⭐ star the repo or fork it.