OpenCC (Open Chinese Convert) is a deterministic, dictionary-based Chinese text conversion library and CLI tool that converts between Simplified Chinese, Traditional Chinese, and regional variants (Taiwan, Hong Kong, Japan), including experimental Small Seal Script (Unicode 18.0). It is used in production by input method engines (RIME, fcitx), dictionary tools (GoldenDict), and NLP pipelines for text normalization and preprocessing. The core is a C++ library with a plugin-based segmentation system (built-in mmseg, optional jieba). Conversion is config-driven via JSON files that define normalization, segmentation, and a chain of dictionary groups with match policies (short_circuit, union). Dictionaries are compiled from text files into binary formats (marisa-trie .ocd2, legacy .ocd). The library exposes C, C++, Python (pybind11), and Node.js (N-API) APIs. A CLI tool provides conversion, inspection, ambiguity detection, and dictionary management. Data flows: text input is segmented, then each segment is looked up in a chain of dictionaries (character, phrase, regional variants). The first match is used (short_circuit) or all matches are unioned. The result is reassembled. The system supports streaming conversion with bounded memory via chunked processing. External dependencies include marisa-trie, darts-clone, rapidjson, pybind11, Google Test, and Google Benchmark; optional jieba (C++ fork) for segmentation plugin.
This repository implements a fully offline, multi-modal translation application for Android. It supports text translation, image translation via OCR (PaddleOCR, Tesseract), document translation (PDF, ODT, EPUB, TXT), live camera translation with GPU-accelerated overlay, screen capture translation, a built-in web browser with adblock and reader mode, text-to-speech (Piper, Kokoro, MMS, Coqui, Mimic3), and dictionary lookup from Wiktionary data. All inference runs on-device using downloaded language packs (translation models, OCR models, TTS voices, dictionaries). The app is built with Kotlin and Jetpack Compose for the UI, with a native Rust core (via UniFFI and JNI) for performance-critical tasks like translation, OCR, TTS synthesis, and GPU rendering. It also includes a Python-based catalog generation pipeline for managing model distributions and a standalone i18n viewer tool for translation management. The architecture is a monolith with multiple foreground services, accessibility services, and overlay windows, all orchestrated by a central ViewModel and a Rust-based catalog handle. While primarily an application, the repository also contains data/research elements in the form of model catalogs, evaluation metrics, and build-time data processing scripts.
Jitendex is a C# rewrite of the build system for a free, offline Japanese-to-English dictionary. It is fundamentally a multi-stage ETL pipeline: raw dictionary archives (JMdict, JMnedict, Kanjidic2, KanjiVG, Tatoeba, CHISE) are imported into SQLite databases, processed into a unified dictionary model with patches, furigana, headwords, cross-references, and integrity checks, then exported for use in Yomitan and MDict. The README explicitly states the rewrite is under construction and not yet production-ready; the legacy Python system still produces published releases. Despite that caveat, the codebase is a serious software engineering effort rather than research code, with console entry points, dependency injection, unit tests, and a modular library structure.
This repository is a comprehensive educational resource that implements a wide range of data structures and algorithms in both JavaScript and TypeScript. It is organized by chapter, mirroring the book 'Learning JavaScript Data Structures and Algorithms' by Loiane Groner. The codebase covers fundamental data structures (arrays, stacks, queues, linked lists, sets, dictionaries, hash tables), trees (binary search trees, AVL trees, red-black trees, Fenwick trees, segment trees), heaps, tries, graphs, and classic algorithms (BFS, DFS, Dijkstra, Floyd-Warshall, Prim, Kruskal, heap sort, etc.). Each implementation is accompanied by Jest tests and many files include both .js and .ts versions. The code is well-structured, uses modern JavaScript/TypeScript features (private fields, generics, arrow functions), and includes JSDoc comments. While primarily educational, the implementations are clean and could serve as reference or be extracted for reuse in other projects. Known minor bugs exist in some hash table collision resolution implementations (e.g., get() always returns undefined in separate chaining, hash function returns NaN in linear probing).