identify Language

API ID 5817

Detects the language of text fragments and returns detailed language information, enhancing global applications with seamless multilingual capabilities.

100% uptime 560 ms avg response

API Documentation

Endpoints

Request

Send a text as a text parameter to the DetectLanguage endpoint.You will receive the detected language and its ISO639 code to integrate into your workflows.

 

 

 

Endpoint ID: 7603
GET https://zylalabs.com/api/5817/identify+language/7603/detect+language
INPUT PARAMETERS

Detect Language — Endpoint Features

Object Description
text Required

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

text
API EXAMPLE RESPONSE
JSON
{"status":"ok","result":"english","result_ISO639":"en","input_type":"text","input":"hello, i need help, can you help me","input_size":35}
Detect Language — CODE SNIPPETS

curl --location --request GET 'https://zylalabs.com/api/5817/identify+language/7603/detect+language?text=hello, i need help, can you help me' --header 'Authorization: Bearer YOUR_API_KEY' 


    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the identify Language simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Overview

About the API:  

The Multilingual Text Identifier API enables fast and accurate language detection for text in over 120 languages. By analyzing a minimum of 10 words, this tool provides precise results, including the language name and its ISO639 code. Ideal for businesses and developers handling international content, the API supports workflows like content adaptation, translation automation, and analytics for diverse audiences. Its user-friendly design ensures easy implementation, making it perfect for real-time applications and systems requiring robust language identification to optimize global communication and improve user interactions.

 

 

identify Language FAQs

The identify Language API is designed to detect the language of text fragments and provide detailed language information, enhancing multilingual capabilities for global applications.

The API supports language detection for over 120 languages, allowing for a wide range of international content handling.

The API requires a minimum of 10 words to analyze in order to provide precise language detection results.

After detecting a language, the API returns the language name along with its ISO639 code, providing detailed language information.

The API is particularly useful for businesses and developers involved in content adaptation, translation automation, and analytics for diverse audiences, as it optimizes global communication and improves user interactions.

The Detect Language endpoint returns data including the detected language name, its ISO639 code, the input type, the original input text, and the size of the input text in characters.

The key fields in the response data include "status," "result" (detected language), "result_ISO639" (ISO code), "input_type," "input" (original text), and "input_size" (character count).

The response data is structured in JSON format, with a top-level object containing "status" and a "result" object that includes language details and input information.

The primary parameter for the Detect Language endpoint is "text," which should contain a minimum of 10 words for accurate detection.

Users can utilize the returned data by integrating the detected language and its ISO639 code into their applications for content adaptation, translation, or analytics, enhancing user experience.

Typical use cases include real-time language detection for chat applications, content localization for websites, and analytics for understanding user demographics based on language preferences.

Data accuracy is maintained through advanced algorithms that analyze linguistic patterns and context, ensuring reliable detection across diverse languages and text types.

If users receive an empty or partial result, they should ensure the input text meets the minimum word requirement and consider providing more context or additional text for better detection.

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