Analyze easily text with AI: Language Detection
Tags: languagedetection, ai, api
Published: 2022-08-05 14:31:43

_This article is brought to you by the Eden AI team. We allow you to test and use in production a large number of AI engines from different providers directly through our API and platform. You are a solution provider and want to integrate Eden AI, contact us at : contact@edenai.co_

## Intro

In this article, we are going to see how we can easily integrate a Language Detection engine in your project and how to choose and access the right engine according to your data.

## Definition

Language detection is the task of automatically detecting the language(s) present in a document based on the content of the document. Using a language detection engine, you can obtain the most likely language for a piece of input text, or a set of possible language candidates with their associated probabilities.

## History

Language detection predates computational methods — the earliest interest in the area was motivated by the needs of translators, and simple manual methods were developed to quickly identify documents in specific languages. The earliest known work to describe a functional Language detection program for text is by Mustonen in 1965, who used multiple discriminant analysis to teach a computer how to distinguish between English, Swedish and Finnish.

In the early 1970s, Nakamura considered the problem of automatic Language detection. His language identifier was able to distinguish between 25 languages written with the Latin alphabet. As features, the method used the occurrence rates of characters and words in each language.

The highest-cited early work on automatic language detection is Cavnar and Trenkle in1994. Cavnar and Trenkle method builds up per-document and per-language profiles, and classifies a document according to which language profile it is most similar to, using a rank-order similarity metric.

## Use cases

You can use Language Detection in numerous fields, here are some examples of common use cases:

- Customer support: detect language of requests to classify them
- Industry: detect documentations languages in order to translate these documentations
- Security: detect language of official documents to verify their authenticity

## The Multi cloud approach

When you need a Language Detection engine, you have 2 options:

- First option: multiple open source Language Detection engines exist, they are free to use. Some of them can be performant but it can be complex to set up and use. Using an open source AI library requires data science expertise. Moreover, you will need to set up a server internally to run open source engines.
- Second option: you can use engines from your cloud provider. Actually, cloud providers like Google Cloud, AWS, Microsoft Azure, Alibaba Cloud or IBM Watson are all providing multiple AI engines often including Language Detection. This option looks very easy because you can stay in a known environment where you might have abilities in your company and the engine is ready-to-use.

The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.

This method is the best in terms of performance and optimization but it presents many inconveniences:

- you may not know every performant providers on the market
- you need to subscribe and contract with all providers
- you need to master each providers API documentation
- you need to check their pricings
- You need to process data in each engine to realize the benchmark

Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Language Detection. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.

Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Language Detection engines with a provider as a simple parameter. With only a few lines, you can set up your project in production.

## Test:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/6ck1yf99y8f9oom4f2b0.png) 
Eden AI python SDK for Language Detection

### Result:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/2cmf06hjbz17njsq67xx.png) 
Eden AI python SDK result for Language Detection

Platform:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/h2ed3sz9gghjp6csoz0w.png) 
Eden AI Platform for Language Detection

## Conclusion:

There are numerous Language Detection engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate Language Detection technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.