Detect Language API
Detect Language API is a language identification service that analyzes text input and returns the language it is written in, along with a confidence score, from a library of over 164 supported languages. Unlike translation APIs that convert text between languages, language detection is the prerequisite step — identifying what language the text is in before deciding what to do with it.
The API is particularly valuable for Nigerian applications because it supports language detection for Yoruba, Igbo, and Hausa — Nigeria's three most widely spoken indigenous languages — as well as Nigerian Pidgin English. This enables Nigerian platforms to build genuinely multilingual experiences where user-submitted content is automatically identified by language and routed appropriately.
The detection algorithm analyzes statistical patterns in the text — character frequency distributions, word patterns, and n-gram features — to identify the most likely language. For most languages with sufficient text, detection accuracy exceeds 95%. The API returns both the top match and alternative candidates with their respective confidence scores, allowing applications to handle ambiguous cases (such as very short texts or mixed-language content) with appropriate fallback behavior.
Batch detection mode accepts multiple text strings in a single API call and returns language identifications for each, making it efficient for processing content in bulk — categorizing a backlog of user-generated posts, classifying customer messages, or tagging a database of documents by language.
For Nigerian social media platforms, community forums, and content sharing applications, language detection enables dynamic routing and display logic. When a user posts in Yoruba, the platform can tag the post with its language, surface it to Yoruba-speaking users in their personalized feed, and potentially offer auto-translation for non-Yoruba speakers. Without automatic language detection, this requires manual tagging by moderators — an approach that does not scale.
Nigerian customer support systems receive incoming messages in multiple languages. A support platform that can automatically detect whether an incoming message is in English, Yoruba, Igbo, Hausa, or Pidgin and route it to the appropriate language-capable agent provides significantly better customer experience than requiring all customers to write in English. Detect Language API makes this routing logic straightforward to implement.
Nigerian news aggregators and media platforms that collect content from diverse sources can use language detection to categorize articles by language — enabling language-filtered views, language-specific newsletters, and content recommendation that respects language preferences.
The API pricing starts at just $4.95/month for 100,000 detections per day, making it very affordable for Nigerian startups and SMEs. The REST API interface is simple — send text, receive language code and confidence score — and client libraries are available for Python, PHP, Ruby, JavaScript, Java, Go, and other languages.
Authentication uses a simple API key passed in the Authorization header, and the free tier provides 1,000 detections per day — sufficient for development and low-volume production use.
Detect Language API's multilingual detection handles code-switched text — content where a writer switches between languages mid-document or mid-sentence, common in Nigerian social media where Yoruba or Igbo phrases are embedded in otherwise English posts. While perfect code-switching detection is challenging for any system, the API's confidence scoring helps applications identify when text contains mixed-language content that may benefit from human review or special handling rather than automatic routing.
The API maintains a detection threshold — minimum confidence level below which it returns null rather than a low-confidence prediction. Nigerian applications can configure their handling of null responses (falling back to a default language, requesting user confirmation, or tagging content for human review) to ensure that ambiguous cases do not produce incorrect downstream actions.