recognizers-text
recognizers-text README
Decision gist · record as of 2026-08-14
Yes, with conditions. The package is permissively licensed and has low install friction, making it suitable for prototyping and integration into Microsoft ecosystem tools. However, it is alpha-stage and has not been updated since 2019; use it for production only if you can accept the maintenance risk and have validated that entity recognition quality meets your requirements for your target languages.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Package is alpha-stage and has not received updates since 2019; language support varies by entity type and culture.
- Low friction install with only two runtime dependencies (emoji, multipledispatch).
- Package is in alpha status and has not been updated since 2019-11-12, though the upstream repository remains active.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you must retain the license notice in distributions.
last release 2019-11-12 (2467 days) · last repo commit 2026-04-17 · 1,793 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 101,349 downloads/mo, #12,943 on PyPI
Alternatives
Verify before relying
pip install recognizers-text
from recognizers_text import recognize_number
results = recognize_number('I have two apples', 'en-us')- Whether the alpha version is suitable for production use and what stability guarantees exist.
- Current state of partial language support (Japanese, Korean, Arabic, Swedish) and whether recognition quality is documented.
- Whether emoji and multipledispatch dependencies are required for all entity types or only specific recognizers.
What it is and what it does
Recognizers-Text is a multilingual entity recognition library that identifies and resolves structured entities—numbers, ordinals, percentages, units (currency, temperature, dimensions, age), dates, times, email addresses, phone numbers, URLs, and more—from natural language text. It powers Microsoft's LUIS, Power Virtual Agents, and Bot Framework, and is also available as a standalone package.
The library targets multiple languages with varying levels of support: full support for English, Chinese, French, Spanish, Portuguese, German, Italian, Turkish, Hindi, and Dutch; partial support for Japanese, Korean, Arabic, and Swedish. It depends on emoji and multipledispatch for runtime operation. The Python package is currently in alpha status and has not been updated since November 2019, though the upstream repository remains active.
Use it for
- Extract structured date and time expressions from user messages in chatbots or voice assistants.
- Parse currency amounts and unit measurements from product descriptions or user input.
- Identify and normalize phone numbers, email addresses, and URLs in text for data cleaning.
- Build multilingual NLP pipelines that need to recognize numbers, ordinals, and percentages across supported languages.
- Pre-process text for downstream ML models by extracting and normalizing temporal and numeric entities.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
The package is permissively licensed and has low install friction, making it suitable for prototyping and integration into Microsoft ecosystem tools. However, it is alpha-stage and has not been updated since 2019; use it for production only if you can accept the maintenance risk and have validated that entity recognition quality meets your requirements for your target languages.
Install
recognizers-text on PyPI
Before you install
Low friction install with only two runtime dependencies (emoji, multipledispatch). Package is in alpha status and has not been updated since 2019-11-12, though the upstream repository remains active.
Package is alpha-stage and has not received updates since 2019; language support varies by entity type and culture.
License in practice
MIT license permits commercial and private use with minimal restrictions; you must retain the license notice in distributions.
Quickstart
pip install recognizers-text
from recognizers_text import recognize_number
results = recognize_number('I have two apples', 'en-us')Verify before relying
- Whether the alpha version is suitable for production use and what stability guarantees exist.
- Current state of partial language support (Japanese, Korean, Arabic, Swedish) and whether recognition quality is documented.
- Whether emoji and multipledispatch dependencies are required for all entity types or only specific recognizers.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesemojimultipledispatch |
| Maintenance | Actively maintained 2,467 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 101,349 / month, #12,943 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.6Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: recognizers_text-1.0.2a2-py3-none-any.whl
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See also recognizers-text-choice · recognizers-text-date-time · recognizers-text-number-with-unit · recognizers-text-number · quantulum3 · presidio-analyzer · date-spacy · gliner2 · number-parser · num2words