  Multi-lingual script identification is a difficult task consisting of
different language with complex backgrounds in scene text images. According to
the current research scenario, deep neural networks are employed as teacher
models to train a smaller student network by utilizing the teacher model's
predictions. This process is known as dark knowledge transfer. It has been
quite successful in many domains where the final result obtained is
unachievable through directly training the student network with a simple
architecture. In this paper, we explore dark knowledge transfer approach using
long short-term memory(LSTM) and CNN based assistant model and various deep
neural networks as the teacher model, with a simple CNN based student network,
in this domain of multi-script identification from natural scene text images.
We explore the performance of different teacher models and their ability to
transfer knowledge to a student network. Although the small student network's
limited size, our approach obtains satisfactory results on a well-known script
identification dataset CVSI-2015.
