Original title: LSTM-Based Autoencoders in Online Handwriting Data Augmentation and Preprocessing
Authors: Gavenciak, Michal
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: 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.
Keywords: autoencoder; Handwriting difficulties; LSTM; XGBoost
Host item entry: Proceedings I of the 30st Conference STUDENT EEICT 2024: General papers, ISBN 978-80-214-6231-1, ISSN 2788-1334

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: https://hdl.handle.net/11012/249237

Permalink: http://www.nusl.cz/ntk/nusl-622568


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