  Script identification plays a significant role in analysing documents and
videos. In this paper, we focus on the problem of script identification in
scene text images and video scripts. Because of low image quality, complex
background and similar layout of characters shared by some scripts like Greek,
Latin, etc., text recognition in those cases become challenging. In this paper,
we propose a novel method that involves extraction of local and global features
using CNN-LSTM framework and weighting them dynamically for script
identification. First, we convert the images into patches and feed them into a
CNN-LSTM framework. Attention-based patch weights are calculated applying
softmax layer after LSTM. Next, we do patch-wise multiplication of these
weights with corresponding CNN to yield local features. Global features are
also extracted from last cell state of LSTM. We employ a fusion technique which
dynamically weights the local and global features for an individual patch.
Experiments have been done in four public script identification datasets:
SIW-13, CVSI2015, ICDAR-17 and MLe2e. The proposed framework achieves superior
results in comparison to conventional methods.
