---
id: recognizers-text-date-time
version: "1.0.2a2"
license: MIT
license_treatment: permissive
maintenance: active
---

# recognizers-text-date-time — recognizers-text-date-time README

License: permissive · Maintenance: active · Downloads: 99.2K/mo

## What it is and what it does

recognizers-text-date-time is a specialized component of Microsoft's Recognizers-Text suite that extracts and normalizes date and time expressions from natural language text. It handles temporal phrases like "tomorrow", "next Friday", "3 PM", and "in two weeks", converting them into structured date/time values. The package supports multiple languages including English, Chinese, French, Spanish, Portuguese, German, Italian, Turkish, Hindi, and Dutch, with partial support for Japanese, Korean, Arabic, and Swedish.

The package is designed as a building block for NLP pipelines, chatbots, and language understanding systems. It depends on recognizers-text as its foundation, along with regex and datedelta for pattern matching and date arithmetic. As an alpha-stage library that has not released since 2019, it represents a snapshot of the recognizer suite's date-time capabilities; the upstream repository remains maintained, but this PyPI package itself is not actively versioned.

Use it for:

- Extract temporal references from user input in chatbots or virtual assistants to schedule tasks or set reminders.
- Parse date/time mentions in customer support tickets or logs to identify when issues occurred.
- Normalize temporal expressions in multilingual documents for downstream analysis or database storage.
- Build NLP preprocessing pipelines that need to identify and resolve date/time entities before further processing.
- Augment text analytics workflows to tag and extract temporal context from unstructured text.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Recognizes and resolves date and time entities from text in multiple languages, extracting temporal expressions and normalizing them to structured date/time values.

Yes, with conditions. The package is stable and permissively licensed, with low install friction and no known vulnerabilities. However, it is in alpha status and has not been released since 2019, which may indicate incomplete language support or unresolved issues. Install if you need multilingual date/time entity extraction in a non-critical context or as part of a larger Recognizers-Text integration; avoid if you require active maintenance or guaranteed stability for production systems.

## Install

pip install recognizers-text-date-time
uv add recognizers-text-date-time
poetry add recognizers-text-date-time

## Installing recognizers-text-date-time

Before you install: Low install friction with a pure-Python wheel. The package is in alpha status (version 1.0.2a2) and has not seen a release since 2019-11-12, though the upstream repository remains active as of 2026-04-17. Depends on recognizers-text and related recognizer packages plus regex and datedelta.

License in practice: MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects without legal concern.

Quickstart:

pip install recognizers-text-date-time

from recognizers_text_date_time import recognize_datetime

results = recognize_datetime('tomorrow at 3pm', 'en-us')
for result in results:
    print(result.resolution)

Package is in alpha; no official Python version specified in metadata, though classifiers indicate Python 3.6+ support. Requires recognizers-text and related packages to be installed.

Verify before relying:

- Actual Python version compatibility beyond classifier hints (3.6+)
- Whether alpha status reflects incomplete implementation or stable-but-unversioned code
- Performance characteristics on large text volumes or complex temporal expressions
- Accuracy rates for date/time extraction across supported languages

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 99.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

date time entity extraction, temporal expression recognition, nlp date parsing, multilingual date recognition, text date time normalization, entity extraction nlp, date entity recognition, nlp-entity-extraction, multilingual, date-time-parsing

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