  We create publicly available language identification (LID) datasets and
models in all 22 Indian languages listed in the Indian constitution in both
native-script and romanized text. First, we create Bhasha-Abhijnaanam, a
language identification test set for native-script as well as romanized text
which spans all 22 Indic languages. We also train IndicLID, a language
identifier for all the above-mentioned languages in both native and romanized
script. For native-script text, it has better language coverage than existing
LIDs and is competitive or better than other LIDs. IndicLID is the first LID
for romanized text in Indian languages. Two major challenges for romanized text
LID are the lack of training data and low-LID performance when languages are
similar. We provide simple and effective solutions to these problems. In
general, there has been limited work on romanized text in any language, and our
findings are relevant to other languages that need romanized language
identification. Our models are publicly available at
https://ai4bharat.iitm.ac.in/indiclid under open-source licenses. Our training
and test sets are also publicly available at
https://ai4bharat.iitm.ac.in/bhasha-abhijnaanam under open-source licenses.
