recognizers-text-number
recognizers-text-number README
Decision gist · record as of 2026-08-14
Yes, if you need multilingual numeric entity extraction and can accept that the package is in alpha status with no updates since 2019. The low install friction, active underlying repository, and MIT license make it a reasonable choice for NLP pipelines. However, verify that the specific language and entity types you need are supported, and be aware that the package itself is not actively maintained.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with only two runtime dependencies (recognizers-text and regex).
- Maintenance status is active with recent commits, though the package itself has not been updated since 2019-11-12.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you must include a copy of the license and copyright 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) · 100,356 downloads/mo, #12,988 on PyPI
Alternatives
Verify before relying
pip install recognizers-text-number
from recognizers_text_number import recognize_number
result = recognize_number("I have twenty-three apples", "en-us")- Whether the package is actively maintained or if 2019-11-12 represents the final release despite the active repository status.
- Current Python version compatibility beyond the stated 3.6 support in classifiers.
- Whether partial language support (Japanese, Korean, Arabic, Swedish) is functional in this number-specific package.
- Specific numeric formats and edge cases supported (e.g., written percentages, range expressions).
What it is and what it does
recognizers-text-number is a language-aware numeric entity recognizer extracted from Microsoft's broader Recognizers-Text project. It identifies and extracts numbers expressed in natural language—cardinals, ordinals, percentages, and ranges—from text and normalizes them to machine-readable form. The package wraps the base recognizers-text library and the regex dependency to provide language-specific parsing rules.
It is designed for NLP pipelines where numeric entities must be identified before downstream processing. The package supports full recognition in English, Chinese, French, Spanish, Portuguese, German, Italian, Turkish, Hindi, and Dutch, with partial support in Japanese, Korean, Arabic, and Swedish. It powers entity extraction in Microsoft's LUIS, Power Virtual Agents, and Bot Framework, and is available as a standalone package for integration into custom applications.
Use it for
- Extract numeric values from user input in chatbots or conversational AI systems for order quantities or date ranges.
- Parse financial or scientific documents to identify amounts, percentages, and measurements in multiple languages.
- Normalize spoken or written numbers in multilingual customer support tickets for downstream analysis.
- Build NLP preprocessing pipelines that require numeric entity recognition before intent classification.
- Validate or standardize numeric input in forms or APIs that accept natural-language number expressions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need multilingual numeric entity extraction and can accept that the package is in alpha status with no updates since 2019.
The low install friction, active underlying repository, and MIT license make it a reasonable choice for NLP pipelines. However, verify that the specific language and entity types you need are supported, and be aware that the package itself is not actively maintained.
Install
recognizers-text-number on PyPI
Before you install
Low friction: pure Python wheel with only two runtime dependencies (recognizers-text and regex). Maintenance status is active with recent commits, though the package itself has not been updated since 2019-11-12.
License in practice
MIT license permits commercial and private use with minimal restrictions; you must include a copy of the license and copyright notice in distributions.
Quickstart
pip install recognizers-text-number
from recognizers_text_number import recognize_number
result = recognize_number("I have twenty-three apples", "en-us")Verify before relying
- Whether the package is actively maintained or if 2019-11-12 represents the final release despite the active repository status.
- Current Python version compatibility beyond the stated 3.6 support in classifiers.
- Whether partial language support (Japanese, Korean, Arabic, Swedish) is functional in this number-specific package.
- Specific numeric formats and edge cases supported (e.g., written percentages, range expressions).
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesrecognizers-textregex |
| Maintenance | Actively maintained 2,467 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 100,356 / month, #12,988 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_number-1.0.2a2-py3-none-any.whl
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See also recognizers-text-choice · recognizers-text · recognizers-text-number-with-unit · recognizers-text-date-time · number-parser · quantulum3 · presidio-analyzer · num2words · cn2an · gliner2