Script identification in natural scene image and video frames using an attention based Convolutional-LSTM network. (January 2019)

• Record Type: Journal Article
• Authors: Bhunia, Ankan Kumar; Konwer, Aishik; Bhunia, Ayan Kumar; Bhowmick, Abir; Roy, Partha P.; Pal, Umapada
• Is Part Of: Pattern recognition. Volume 85(2019:Jan.)
• Page Start: 172
• Page End: 184
• Publication Date: 2019-01
• Subjects: Script identification -- Convolutional neural network -- Long short-term memory -- Local feature -- Global feature -- Attention network -- Dynamic weighting; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.07.034
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A new attention based CNN-LSTM network is proposed for script identification. It is based on local and global feature extraction and dynamically weighting them. Attention used twice to give priority to significant features and to focus on more relevant patches. The method has been tested on SIW-13, CVSI-2015, ICDAR-17 and MLe2e datasets. Abstract: 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.