A Survey on Handwriting Disorder Detection in Individuals with Disabilities

Abstract: Handwritten writing is a complex biometric marker, which reflects both neurometer skill and cognitive processing abilities. The growing occurrence of writing and learning difficulties such as dysgraphia and dyslexia along with neurological conditions such as Parkinson's disease (PD), Multiple Sclerosis (MS), essential tremor (ET), and Alzheimer's disease (AD). There has been significant interest in automated handwritten based diagnostic and assessment systems. This research conducted a detailed systematic assessment of AI and Machine Learning approaches applied to handwritten analysis for prediction severity classification and monitoring of these issues. The comprehensive studies involve a broad range of approaches including CNN, transfer learning, ensemble methods, multimodal fusion frameworks, and data augmentation techniques. All analyses consistently compare the main Key performance measures including accuracy, precision, recall F1-score area under the curve of ROC sensitivity and specificity. The H2FCD model, CNN and SVM hybrid achieved highest accuracy of 99.26% and 99.33% as a result. The research gap is identified highlighting the lack of Multilanguage and multiclass severity datasets, absence of real world clinical deployment pipelines and the underrepresentation of diverse populations in training data. This review supports AI- based handwriting analysis for disability evaluation.

Keywords: Dysgraphia, Handwriting Analysis, Machine Learning, CNN, Feature Extraction