You can use ML Kit to identify the language of a string of text. You can get the string's most likely language as well as confidence scores for all of the string's possible languages. ML Kit recognizes text in more than 100 different languages in their native scripts. In addition, romanized text can be recognized for Arabic, Bulgarian, Chinese, Greek, Hindi, Japanese, and Russian. See the [complete list](https://developers.google.com/ml-kit/language/identification/langid-support.md.txt) of supported languages and scripts.
This API is available using either an unbundled library that must be downloaded before use or a bundled library that increases your app size. See [this guide](https://developers.google.com/ml-kit/tips/installation-paths.md.txt) for more information on the differences between the two installation options. | | Bundled | Unbundled | |---|---|---| | **Library name** | `com.google.mlkit:language-id` | `com.google.android.gms:play-services-mlkit-language-id` | | **Implementation** | Model is statically linked to your app at build time. | Model is dynamically downloaded via Google Play Services. | | **App size impact** | About 900 KB size increase. | About 200 KB size increase. | | **Initialization time** | Model is available immediately. | Might have to wait for model to download before first use. | ## Try it out - Play around with [the sample app](https://github.com/googlesamples/mlkit/tree/master/android/langid) to see an example usage of this API. ## Before you begin > [!NOTE] > This API requires Android API level 23 or above. Make sure that your app's build file uses a `minSdkVersion` value of 23 or higher. 1. In your project-level `build.gradle` file, make sure to include Google's Maven repository in both your `buildscript` and `allprojects` sections. 2. Add the dependencies for the ML Kit Android libraries to your module's app-level gradle file, which is usually `app/build.gradle`. Choose one of the following dependencies based on your needs: **For bundling the model with your app:** dependencies { // ... // Use this dependency to bundle the model with your app implementation 'com.google.mlkit:language-id:17.0.6' } **For using the model in Google Play Services:** dependencies { // ... // Use this dependency to use the dynamically downloaded model in Google Play Services implementation 'com.google.android.gms:play-services-mlkit-language-id:17.0.0' } 3. **If you choose to use the model in Google Play Services** , you can configure your app to automatically download the model to the device after your app is installed from the Play Store. To do so, add the following declaration to your app's `AndroidManifest.xml` file: ... You can also explicitly check the model availability and request download through Google Play services [ModuleInstallClient API](https://developers.google.com/android/guides/module-install-apis.md.txt). If you don't enable install-time model downloads or request explicit download, the model is downloaded the first time you run the identifier. Requests you make before the download has completed produce no results. ## Identify the language of a string To identify the language of a string, call `LanguageIdentification.getClient()` to get an instance of `LanguageIdentifier`, and then pass the string to the `identifyLanguage()` method of `LanguageIdentifier`. For example: ### Kotlin ```kotlin val languageIdentifier = LanguageIdentification.getClient() languageIdentifier.identifyLanguage(text) .addOnSuccessListener { languageCode -> if (languageCode == "und") { Log.i(TAG, "Can't identify language.") } else { Log.i(TAG, "Language: $languageCode") } } .addOnFailureListener { // Model couldn't be loaded or other internal error. // ... } ``` ### Java ```java LanguageIdentifier languageIdentifier = LanguageIdentification.getClient(); languageIdentifier.identifyLanguage(text) .addOnSuccessListener( new OnSuccessListener() { @Override public void onSuccess(@Nullable String languageCode) { if (languageCode.equals("und")) { Log.i(TAG, "Can't identify language."); } else { Log.i(TAG, "Language: " + languageCode); } } }) .addOnFailureListener( new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { // Model couldn't be loaded or other internal error. // ... } }); ``` If the call succeeds, a [BCP-47 language code](https://en.wikipedia.org/wiki/IETF_language_tag) is passed to the success listener, indicating the language of the text. If no language is confidently detected, the code [`und`](https://tools.ietf.org/html/bcp47#section-4.1) (undetermined) is passed. By default, ML Kit returns a value other than `und` only when it identifies the language with a confidence value of at least 0.5. You can change this threshold by passing a `LanguageIdentificationOptions` object to `getClient()`: ### Kotlin ```kotlin val languageIdentifier = LanguageIdentification .getClient(LanguageIdentificationOptions.Builder() .setConfidenceThreshold(0.34f) .build()) ``` ### Java ```java LanguageIdentifier languageIdentifier = LanguageIdentification.getClient( new LanguageIdentificationOptions.Builder() .setConfidenceThreshold(0.34f) .build()); ``` ## Get the possible languages of a string To get the confidence values of a string's most likely languages, get an instance of `LanguageIdentifier` and then pass the string to the `identifyPossibleLanguages()` method. For example: ### Kotlin ```kotlin val languageIdentifier = LanguageIdentification.getClient() languageIdentifier.identifyPossibleLanguages(text) .addOnSuccessListener { identifiedLanguages -> for (identifiedLanguage in identifiedLanguages) { val language = identifiedLanguage.languageTag val confidence = identifiedLanguage.confidence Log.i(TAG, "$language $confidence") } } .addOnFailureListener { // Model couldn't be loaded or other internal error. // ... } ``` ### Java ```java LanguageIdentifier languageIdentifier = LanguageIdentification.getClient(); languageIdentifier.identifyPossibleLanguages(text) .addOnSuccessListener(new OnSuccessListener>() { @Override public void onSuccess(List identifiedLanguages) { for (IdentifiedLanguage identifiedLanguage : identifiedLanguages) { String language = identifiedLanguage.getLanguageTag(); float confidence = identifiedLanguage.getConfidence(); Log.i(TAG, language + " (" + confidence + ")"); } } }) .addOnFailureListener( new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { // Model couldn't be loaded or other internal error. // ... } }); ``` If the call succeeds, a list of `IdentifiedLanguage` objects is passed to the success listener. From each object, you can get the language's BCP-47 code and the confidence that the string is in that language. Note that these values indicate the confidence that the entire string is in the given language; ML Kit doesn't identify multiple languages in a single string. By default, ML Kit returns only languages with confidence values of at least 0.01. You can change this threshold by passing a `LanguageIdentificationOptions` object to `getClient()`: ### Kotlin ```kotlin val languageIdentifier = LanguageIdentification .getClient(LanguageIdentificationOptions.Builder() .setConfidenceThreshold(0.5f) .build()) ``` ### Java ```java LanguageIdentifier languageIdentifier = LanguageIdentification.getClient( new LanguageIdentificationOptions.Builder() .setConfidenceThreshold(0.5f) .build()); ``` If no language meets this threshold, the list has one item, with the value [`und`](https://tools.ietf.org/html/bcp47#section-4.1).