  People with visual impairments have difficulty accessing touchscreen-enabled
personal computing devices like mobile phones and laptops. The image-to-speech
(ITS) systems can assist them in mitigating this problem, but their huge model
size makes it extremely hard to be deployed on low-resourced embedded devices.
In this paper, we aim to overcome this challenge by developing an efficient
endto-end neural architecture for generating audio from tiny segments of
display content on low-resource devices. We introduced a vision
transformers-based image encoder and utilized knowledge distillation to
compress the model from 6.1 million to 2.46 million parameters. Human and
automatic evaluation results show that our approach leads to a very minimal
drop in performance and can speed up the inference time by 22%.
