Text readability assessment has a wide range of applications for different
target people, from language learners to people with disabilities. The fast
pace of textual content production on the web makes it impossible to measure
text complexity without the benefit of machine learning and natural language
processing techniques. Although various research addressed the readability
assessment of English text in recent years, there is still room for improvement
of the models for other languages. In this paper, we proposed a new model for
text complexity assessment for German text based on transfer learning. Our
results show that the model outperforms more classical solutions based on
linguistic features extraction from input text. The best model is based on the
BERT pre-trained language model achieved the Root Mean Square Error (RMSE) of
0.483.

本研究基于迁移学习提出了一种新的德文文本复杂度评估模型，结果表明该模型优于传统的基于文本语言特征提取的解决方案，最佳模型使用 BERT 预训练语言模型取得了 0.483 的均方根误差。

基于迁移学习的德语文本可读性评估模型

A Transfer Learning Based Model for Text Readability Assessment in German

Multispectral imaging is an important technique for improving the readability
of written or printed text where the letters have faded, either due to
deliberate erasing or simply due to the ravages of time. Often the text can be
read simply by looking at individual wavelengths, but in some cases the images
need further enhancement to maximise the chances of reading the text. There are
many possible enhancement techniques and this paper assesses and compares an
extended set of dimensionality reduction methods for image processing. We
assess 15 dimensionality reduction methods in two different manuscripts. This
assessment was performed both subjectively by asking the opinions of scholars
who were experts in the languages used in the manuscripts which of the
techniques they preferred and also by using the Davies-Bouldin and Dunn indexes
for assessing the quality of the resulted image clusters. We found that the
Canonical Variates Analysis (CVA) method which was using a Matlab
implementation and we have used previously to enhance multispectral images, it
was indeed superior to all the other tested methods. However it is very likely
that other approaches will be more suitable in specific circumstance so we
would still recommend that a range of these techniques are tried. In
particular, CVA is a supervised clustering technique so it requires
considerably more user time and effort than a non-supervised technique such as
the much more commonly used Principle Component Analysis Approach (PCA). If the
results from PCA are adequate to allow a text to be read then the added effort
required for CVA may not be justified. For the purposes of comparing the
computational times and the image results, a CVA method is also implemented in
C programming language and using the GNU (GNUs Not Unix) Scientific Library
(GSL) and the OpenCV (OPEN source Computer Vision) computer vision programming
library.

本文研究了 15 种降维方法在多光谱图像增强中的应用，通过主客观方法评估结果，发现 Matlab 实现的 CVA 方法优于其他方法。此外，本文还提出了一种用 C 编程语言实现的 CVA 方法，比较了 CVA 和 PCA 的计算时间和图像结果。