Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc. interfere with academic achievements but have also long terms consequences beyond the academic time. It is widely admitted that between 5% to 10% of the world population is subject to this kind of disabilities. For assessing such disabilities in early childhood, children have to solve a battery of tests. Human experts score these tests, and decide whether the children require specific education strategy on the basis of their marks. The assessment can be lengthy, costly and emotionally painful. In this paper, we investigate how Artificial Intelligence can help in automating this assessment. Gathering a dataset of handwritten text pictures and audio recordings, both from standard children and from dyslexic and/or dysgraphic children, we apply machine learning techniques for classification in order to analyze the differences between dyslexic/dysgraphic and standard readers/writers and to build a model. The model is trained on simple features obtained by analysing the pictures and the audio files. Our preliminary implementation shows relatively high performances on the dataset we have used. This suggests the possibility to screen dyslexia and dysgraphia via non-invasive methods in an accurate way as soon as enough data are available.

中文翻译：

---

阅读障碍和书写障碍预测：一种新的机器学习方法

学习障碍，如书写困难、阅读障碍、使用障碍等，会干扰学业成绩，但也会在学业时间之外产生长期后果。人们普遍承认，世界上有 5% 到 10% 的人口患有这种残疾。为了评估儿童早期的此类残疾，儿童必须解决一系列测试。人类专家对这些测试进行评分，并根据孩子的分数决定是否需要特定的教育策略。评估可能是漫长的、昂贵的并且在情感上是痛苦的。在本文中，我们研究了人工智能如何帮助实现此评估的自动化。收集来自标准儿童和阅读障碍和/或阅读障碍儿童的手写文本图片和录音数据集，我们应用机器学习技术进行分类，以分析阅读障碍/阅读障碍与标准读者/作者之间的差异并构建模型。该模型基于通过分析图片和音频文件获得的简单特征进行训练。我们的初步实现在我们使用的数据集上表现出相对较高的性能。这表明一旦有足够的数据可用，就有可能通过非侵入性方法以准确的方式筛查阅读障碍和书写困难。