Script Identification in Natural Scene Image and Video Frame using Attention based Convolutional-LSTM Network

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. Most of the recent approaches generally use a patch-based CNN network with summation of obtained features, or only a CNN-LSTM network to get the identification result. Some use a discriminative CNN to jointly optimize mid-level representations and deep features. 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. Then 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 two public script identification datasets, SIW-13 and CVSI2015. The proposed framework achieves superior results in comparison to conventional methods. Keywords-Script Identification, Convolutional Neural Network, Long Short-Term Memory, Local feature, Global feature, Attention Network, Dynamic Weighting. 1 1 # Both the authors contributed equally.