Parkinson’s Disease Detection through Offline Handwriting Analysis

A CNN-Based Approach

Abstract

This study investigates the use of offline handwriting analysis for the automated diagnosis of Parkinson’s disease (PD) using deep learning techniques. A convolutional neural network (CNN) was designed to extract discriminative spatial features from handwritten images and classify subjects as either PD patients or healthy controls. The model was evaluated on four publicly available datasets—HandPD, NewHandPD, PaHaW, and UCI—representing a diverse range of handwriting patterns and acquisition conditions. The proposed CNN achieved 100% accuracy on the smaller UCI dataset and 94.74% accuracy on the larger NewHandPD dataset. To overcome dataset imbalance and limited sample diversity, various data augmentation strategies were applied, leading to a notable increase in overall performance, with accuracies exceeding 97% on larger datasets. These results demonstrate that offline handwriting analysis, supported by deep CNN architectures and data augmentation, offers a promising, non-invasive, and cost-effective approach for early PD diagnosis and potential continuous monitoring. Furthermore, this study aligns with broader advances in AI-assisted medical diagnostics, reinforcing the role of machine learning and image-based analysis in healthcare applications.