{"categories":[{"label":"Linguistic","url":"https://skillfed.io/packages/category/text-processing-linguistic/2"}],"enrichment":{"capability":"Cutlet converts Japanese text to romaji (Latin characters) using multiple romanization systems including Modified Hepburn, Kunrei-shiki, and Nihon-shiki, with support for proper noun capitalization and URL slug generation.","skillfed_tags":["japanese-nlp","text-conversion","romanization"],"use_cases":["Generate URL slugs from Japanese product or article titles for web applications","Convert Japanese names and addresses to romanized form for international databases or forms","Build search or filtering systems that accept both Japanese and romanized input","Create phonetic transcriptions of Japanese text for accessibility or educational purposes","Integrate romanization into NLP pipelines that process mixed-language content"],"what_it_does":"Cutlet is a Python library that converts Japanese text into Latin-character romanization. It supports three major romanization systems (Modified Hepburn, Kunrei-shiki, and Nihon-shiki) and includes features like proper noun capitalization, foreign spelling detection via UniDic, and URL-safe slug generation. The library uses fugashi for morphological analysis, meaning it shares the same tokenization and dictionary infrastructure as other Japanese NLP tools.\n\nThe package is useful when you need to make Japanese text readable to audiences unfamiliar with Japanese characters, generate URL-safe versions of Japanese names or phrases, or integrate romanization into broader Japanese language processing pipelines. It handles common exceptions (Tokyo, Osaka) and allows custom overrides for individual words or character mappings.","worth_installing":"Yes. Cutlet is actively maintained, has no known vulnerabilities, installs with low friction, and solves a specific problem well. Use it if you need to romanize Japanese text; the permissive MIT license and moderate popularity (top 15000 packages) suggest it is stable and reliable for production use. Verify that your environment can satisfy the MeCab dictionary requirement before deploying."},"id":"cutlet","links":{"html":"https://skillfed.io/packages/cutlet","md":"https://skillfed.io/packages/cutlet.md","pypi":"https://pypi.org/project/cutlet/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-01","license_spdx":null,"license_treatment":"permissive","name":"cutlet","python_support":"supports_current","summary":"Romaji converter"},"popularity":{"monthly_downloads":110368,"position":12466,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.2"}