  In this paper, we propose a novel approach of word-level Indic script
identification using only character-level data in training stage. The
advantages of using character level data for training have been outlined in
section I. Our method uses a multimodal deep network which takes both offline
and online modality of the data as input in order to explore the information
from both the modalities jointly for script identification task. We take
handwritten data in either modality as input and the opposite modality is
generated through intermodality conversion. Thereafter, we feed this
offline-online modality pair to our network. Hence, along with the advantage of
utilizing information from both the modalities, it can work as a single
framework for both offline and online script identification simultaneously
which alleviates the need for designing two separate script identification
modules for individual modality. One more major contribution is that we propose
a novel conditional multimodal fusion scheme to combine the information from
offline and online modality which takes into account the real origin of the
data being fed to our network and thus it combines adaptively. An exhaustive
experiment has been done on a data set consisting of English and six Indic
scripts. Our proposed framework clearly outperforms different frameworks based
on traditional classifiers along with handcrafted features and deep learning
based methods with a clear margin. Extensive experiments show that using only
character level training data can achieve state-of-art performance similar to
that obtained with traditional training using word level data in our framework.
