Text Readability

Augmented Reality is a promising technology for the product lifecycle development, but it is still not established in industrial facilities. The most relevant issues to be addressed relate to the ergonomics: avoid the discomfort of Head-Worn Displays, allow the operators to have free hands and improve data visualization. In this work we study the possibility to use projection-based Augmented Reality (projected AR), as optimal solution for technical visualization on industrial workbenches. In particular, text legibility in projected AR is difficult to optimize since it is affected by many parameters: environment conditions, text style, material and shape of the target surface. This problem is poorly addressed in literature and in the specific industrial field. We analyze the legibility of a set of colors prescribed by international standards for the industrial environments, on six widely used industrial workbenches surfaces. We compared the performance of 14 subjects using projected AR, with that using a traditional LCD monitor. We collected about 2500 measurements (times and errors) through the use of a test application, followed by qualitative interviews. The results showed that, as regards legibility, projected AR can be used in place of traditional monitors in most of the cases. Another not trivial finding is that the influence on legibility of surface irregularities (e.g., grooves, prominences) is more important than that of surface texturization. A possible limitation for the use of projected AR is given by the blue color, whose performance turned out to be lower than that of other colors with every workbench surface.

Studies on simple language and simplification are often based on datasets of texts, either for children or learners of a second language. In both cases, these texts represent an example of simple language, but simplification likely involves different strategies. As such, this data may not be entirely homogeneous in terms of text simplicity. This study investigates linguistic properties and specific simplification strategies used in Russian texts for primary school children with different language backgrounds and levels of language proficiency. To explore the structure and variability of simple texts for young readers of different age groups, we have trained models for multiclass and binary classification. The models were based on quantitative features of texts. Subsequently, we evaluated the simplification strategies applied to readers of the same age with different linguistic backgrounds. This study is particularly relevant for the Russian language material, where the concept of easy and plain language has not been sufficiently investigated. The study revealed that the three types of texts cannot easily be distinguished from each other by judging the performance of multiclass models based on various quantitative features. Therefore, it can be said that texts of all types exhibit a similar level of accessibility to young readers. In contrast, binary classification tasks demonstrated better results, especially in the R-native vs. non R-native track (with 0.78 F1-score), these results may indicate that the strategies used for adapting or creating texts for each type of audience are different.

This presentation examines briefly a few of the linguistic properties which can be shown to interfere with the ability of readers to comprehend texts: (1) extraposition and self-embedding, (2) the semantic effects of “garden path” constructions, and (3) the difficulty in processing narratives containing embedded narratives.  Our goal is to show that applying linguistic concepts and insights to the issue of text difficulty can help us to develop a better understanding of what makes a text easier or more difficult to read.