  This paper presents a novel methodology of Indic handwritten script
recognition using Recurrent Neural Networks and addresses the problem of script
recognition in poor data scenarios, such as when only character level online
data is available. It is based on the hypothesis that curves of online
character data comprise sufficient information for prediction at the word
level. Online character data is used to train RNNs using BLSTM architecture
which are then used to make predictions of online word level data. These
prediction results on the test set are at par with prediction results of models
trained with online word data, while the training of the character level model
is much less data intensive and takes much less time. Performance for
binary-script models and then 5 Indic script models are reported, along with
comparison with HMM models.The system is extended for offline data prediction.
Raw offline data lacks the temporal information available in online data and
required for prediction using models trained with online data. To overcome
this, stroke recovery is implemented and the strokes are utilized for
predicting using the online character level models. The performance on
character and word level offline data is reported.
