I Can't Believe It's Not Better: In-air Movement For Alzheimer
  Handwriting Synthetic Generation
Asma Bensalah Antonio Parziale Giuseppe De Gregorio Angelo Marcelli Alicia Fornés Lladós
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.