In past, we had Google Translate to translate text from one language to another. Of course, the translation output was not as good as it is today when AI is used for translating. The translation was based on statistical techniques to detect patterns between the two languages.

After the arrival of neural networks, the translation has become neural. There is encoding of sequences as the input. Then there is decoding of these sequences as the output in the target language. The decoding happens as the model has been trained on vast amount of data of multiple languages. The model uses attention mechanism too. The model learns the nuances and dependencies of these languages and is able to translate even idiomatic sequences from the source language to the target language.

Many language models are able to translate in 100 plus languages. Neural translation has supplanted statistical translation.

India has set up Bhashini, a language translation and database platform. It is doing proof-of-concepts for multi-lingual call centers or IVR setups. It will shortly offer real-time language translation services on a paid basis.

An app will be launched to demo real-time translation in open format.

Bhashini has been developed by the Digital India Bhashini division under the Digital India Corporation as a section 8 company. It can do text-to-text translation in 22 languages. It has the capability to recognize automated speech, text-to-speech synthesis, OCR, video translation, document translation, language detection, voice-based payments, among others.

It also provides API (application programming interface) integration to startups.

It handles, at present, 40 million inferences per month. It means translations happening on the platform by users using its different features.

It also wants to facilitate e-commerce through ONDC.

Bhashini collects datasets from crowdsourcing model called Bhashadaan.