The field of linguistic analysis and handwriting research is moving towards a more comprehensive understanding of the complex relationships between language, cognition, and motor skills. Recent studies have explored the use of machine learning techniques to analyze palmar features and handwriting patterns, revealing new insights into the correlations between these characteristics and various traits or conditions. The development of large-scale databases and toolboxes, such as those for Chinese character handwriting and Persian offline handwritten texts, has also facilitated research in this area. Furthermore, investigations into the cognitive reality of inflectional classes and the role of meaning in lexical processing have shed light on the underlying mechanisms of language processing. Noteworthy papers include: Palmistry-Informed Feature Extraction and Analysis using Machine Learning, which demonstrates the feasibility of automated palmar feature analysis. A Stroke-Level Large-Scale Database of Chinese Character Handwriting and the OpenHandWrite_Toolbox for Handwriting Research, which provides a valuable resource for psycholinguistic and neurolinguistic research on handwriting.