  With the increasing number of mobile devices, there has been continuous
research on generating optimized Language Models (LMs) for soft keyboard. In
spite of advances in this domain, building a single LM for low-end feature
phones as well as high-end smartphones is still a pressing need. Hence, we
propose a novel technique, Optimized N-gram (Op-Ngram), an end-to-end N-gram
pipeline that utilises mobile resources efficiently for faster Word Completion
(WC) and Next Word Prediction (NWP). Op-Ngram applies Stupid Backoff and
pruning strategies to generate a light-weight model. The LM loading time on
mobile is linear with respect to model size. We observed that Op-Ngram gives
37% improvement in Language Model (LM)-ROM size, 76% in LM-RAM size, 88% in
loading time and 89% in average suggestion time as compared to SORTED array
variant of BerkeleyLM. Moreover, our method shows significant performance
improvement over KenLM as well.
