  Optical Character Recognition (OCR) systems have been widely used in various
applications for extracting semantic information from images. To give the user
more control over their privacy, an on-device solution is needed. The current
state-of-the-art models are too heavy and complex to be deployed on-device. We
develop an efficient lightweight scene text recognition (STR) system, which has
only 0.88M parameters and performs real-time text recognition. Attention
modules tend to boost the accuracy of STR networks but are generally slow and
not optimized for device inference. So, we propose the use of convolution
attention modules to the text recognition networks, which aims to provide
channel and spatial attention information to the LSTM module by adding very
minimal computational cost. It boosts our word accuracy on ICDAR 13 dataset by
almost 2\%. We also introduce a novel orientation classifier module, to support
the simultaneous recognition of both horizontal and vertical text. The proposed
model surpasses on-device metrics of inference time and memory footprint and
achieves comparable accuracy when compared to the leading commercial and other
open-source OCR engines. We deploy the system on-device with an inference speed
of 2.44 ms per word on the Exynos 990 chipset device and achieve an accuracy of
88.4\% on ICDAR-13 dataset.
