The objective of this work is to introduce text simplification as a potential reading aid to help improve the poor reading performance experienced by visually impaired individuals. As a first step, we explore what makes a text especially complex when read with low vision, by assessing the individual effect of three word properties (frequency, orthographic similarity and length) on reading speed in the presence of Central visual Field Loss (CFL). Individuals with bilateral CFL induced by macular diseases read pairs of French sentences displayed with the self-paced reading method. For each sentence pair, sentence n contained a target word matched with a synonym word of the same length included in sentence n+1. Reading time was recorded for each target word. Given the corpus we used, our results show that (1) word frequency has a significant effect on reading time (the more frequent the faster the reading speed) with larger amplitude (in the range of seconds) compared to normal vision; (2) word neighborhood size has a significant effect on reading time (the more neighbors the slower the reading speed), this effect being rather small in amplitude, but interestingly reversed compared to normal vision; (3) word length has no significant effect on reading time. Supporting the development of new and more effective assistive technology to help low vision is an important and timely issue, with massive potential implications for social and rehabilitation practices. The end goal of this project will be to use our findings to custom text simplification to this specific population and use it as an optimal and efficient reading aid.

In this paper, we propose a new strategy, called Text Deconvolution Saliency (TDS), to visualize linguistic information detected by a CNN for text classification. We extend Deconvolution Networks to text in order to present a new perspective on text analysis to the linguistic community. We empirically demonstrated the efficiency of our Text Deconvolution Saliency on corpora from three different languages: English, French, and Latin. For every tested dataset, our Text Deconvolution Saliency automatically encodes complex linguistic patterns based on co-occurrences and possibly on grammatical and syntax analysis.