Handwriting Analysis for Personality Assessment

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Project Overview:

Objective

The “Handwriting Analysis for Personality Assessment” project aims to create a dataset for training machine learning models to analyze and assess personality traits based on handwriting samples. This dataset, specifically tailored for the Handwriting Recognition for Digital Note-Taking in the Wild initiative, will support research in graphology and contribute to the development of handwriting-based personality assessment tools.

Scope

This project involves collecting handwriting samples from individuals for Handwriting Recognition for Digital Note-Taking. Additionally, self-reported personality traits are gathered and annotated with personality trait labels based on graphological analysis.

Sources

Volunteers: Recruit volunteers to provide handwriting samples, and then have them complete personality assessment questionnaires.
Handwritten Documents: Collect handwritten documents, such as letters or essays, from individuals, thereby gathering valuable insights into personal communication and expression.

Data Collection Metrics

Total Handwriting Samples for Personality Assessment: 5,000 samples
Volunteers: 3,000
Handwritten Documents: 2,000

Annotation Process

Stages

Personality Assessment: Furthermore, each handwriting sample will be annotated with personality trait labels based on graphological analysis, including extroversion, introversion, conscientiousness, or emotional stability.
Metadata Logging: Additionally, metadata logging includes recording details such as the participant’s age, gender, handedness, and any relevant information about the handwriting samples.

Annotation Metrics

Handwriting Samples with Personality Trait Labels: 5,000
Metadata Logging: 5,000

Quality Assurance

Stages

Annotation Verification: Implementing a validation process involving graphologists or experts in handwriting analysis will ensure the accuracy of personality trait labels. Additionally, by incorporating their expertise, we can thoroughly review and verify the assigned traits. This is especially crucial in contexts like Handwriting Recognition for Digital Note-Taking, where precise labeling is essential for effective analysis.
Data Quality Control: Additionally, ensure the removal of handwriting samples with illegible or incomplete writing, as well as those that do not align with the project’s objectives.
Data Security:Moreover, protect sensitive participant information and adhere strictly to privacy regulations.

QA Metrics

Annotation Validation Cases: 500 (10% of total)
Data Cleansing: To improve clarity and relevance, we will remove illegible or irrelevant handwriting samples from the collection. Additionally, we will utilize transition words to ensure a smoother flow throughout the content.

Conclusion

The “Handwriting Analysis for Personality Assessment” dataset serves as a valuable resource for researchers and developers engaged in personality assessment and graphology-related projects. With accurately annotated handwriting samples and comprehensive metadata, this dataset empowers the development of machine learning models and tools capable of analyzing and assessing personality traits based on handwriting characteristics. Additionally, it contributes to the exploration of handwriting analysis as a potential method for personality assessment, offering insights into the relationship between handwriting and personality traits.