Název: LSTM-Based Autoencoders in Online Handwriting Data Augmentation and Preprocessing
Autoři: Gavenciak, Michal
Typ dokumentu: Příspěvky z konference
Jazyk: eng
Nakladatel: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstrakt: On-line handwriting analysis is a research field that is among others used in assessment of handwriting difficulties (HD), which can be manifestations of degenerative brain diseases such as Parkinson’s disease in the elderly, or developmental dysgraphia in children. Using advanced modelling approaches or artificial intelligence is often difficult because of the limited data availability in both demographic cohorts. In this article, a data processing approach, using LSTM-based autoencoders, is described as a way of augmenting the database with semisynthetic data or preprocessing the data to improve the performance of feature-based classification. The proposed method has led to a 3 percentage point increase in classification accuracy when compared to baseline. While the improvement is marginal, it highlights another possible area of research to improve the efficacy of automated HD assessment.
Klíčová slova: autoencoder; Handwriting difficulties; LSTM; XGBoost
Zdrojový dokument: Proceedings I of the 30st Conference STUDENT EEICT 2024: General papers, ISBN 978-80-214-6231-1, ISSN 2788-1334

Instituce: Vysoké učení technické v Brně (web)
Informace o dostupnosti dokumentu: Plný text je dostupný v Digitální knihovně VUT.
Původní záznam: https://hdl.handle.net/11012/249237

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-622568