Abstract
During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer's disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation.
Abstract (translated)
在最近几年，深度学习在手写分析和识别领域的应用呈现爆炸式增长。手写分析的一个主要应用是医学领域中的早期检测和诊断。然而，大多数真实病例问题仍然缺乏数据，这使得基于深度学习的模型的应用变得困难。为了解决这个问题，一些工作求助于合成数据生成。近年来，更多的研究将方向转向了指导式数据合成，这是一种利用领域和数据知识来生成现实数据以供训练深度学习模型使用的生成方法。在这项工作中，我们结合了关于阿尔茨海默病手写的领域知识，并将其应用于更指导性的数据生成。具体来说，我们研究了使用空气运动进行合成数据生成的应用。
URL
https://arxiv.org/abs/2312.05086
