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Automatic identification of script is an essential component of amultilingual OCR engine. In this paper, we present an efficient, lightweight,real-time and on-device spatial attention based CNN-LSTM network for scene textscript identification, feasible for deployment on resource constrained mobiledevices. Our network consists of a CNN, equipped with a spatial attentionmodule which helps reduce the spatial distortions present in natural images.This allows the feature extractor to generate rich image representations whileignoring the deformities and thereby, enhancing the performance of this finegrained classification task. The network also employs residue convolutionalblocks to build a deep network to focus on the discriminative features of ascript. The CNN learns the text feature representation by identifying eachcharacter as belonging to a particular script and the long term spatialdependencies within the text are captured using the sequence learningcapabilities of the LSTM layers. Combining the spatial attention mechanism withthe residue convolutional blocks, we are able to enhance the performance of thebaseline CNN to build an end-to-end trainable network for scriptidentification. The experimental results on several standard benchmarksdemonstrate the effectiveness of our method. The network achieves competitiveaccuracy with state-of-the-art methods and is superior in terms of networksize, with a total of just 1.1 million parameters and inference time of 2.7milliseconds.

On-Device Spatial Attention based Sequence Learning Approach for Scene Text Script Identification