# Detection of Personality Traits Using Handwriting and Deep Learning
## 2 Related Work
### Computer-Assisted Techniques Used for Handwriting Analysis  
With the development of image-processing algorithms, numerous results have been reported on the determination of certain personality traits through computer-assisted analysis. Studies in the field of personality traits' detection based on handwriting analysis using machine learning and deep learning generally exhibit the following characteristics:  
* They explore links between handwriting patterns and psychological or behavioral tendencies [18]. Most studies are grounded in widely accepted frameworks like the Big Five (OCEAN), MBTI, or HEXACO, providing a structured approach to trait analysis. * The data are collected from diverse participants, in the form of written text and signatures. Data often include age, gender, cultural background, and sometimes psychological assessments [19]. * The handwriting characteristics analyzed can be classified into three categories:  
Geometric: Stroke length, slant, size, spacing, pressure, and alignment [20]. Kinematic: Speed, acceleration, and smoothness of pen movement [21]. Graphological: Style-specific traits like loops, flourishes, and baseline adherence [22]. The main evaluation metrics of the proposed algorithms are accuracy, precision, recall, F1 score, and sometimes correlation coefficients to measure alignment with psychological assessments. Automatic personality detection algorithms can be classified into two broad categories: machine learning algorithms and deep learning algorithms. The first category includes classification methods based on algorithms such as Support Vector Machine (SVM), AdaBoost, and k-nearest neighbors (KNN). The second category (based on deep learning) includes algorithms that use multilayer perceptron neural networks (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), or Gated Recurrent Units (GRUs). So, in [23], extracted features from handwriting are mapped with personality using the rule-based technique. In [24], the authors implement a series of empirical algorithms for spatial analysis of text. Personality traits are inferred based on the distance between lines, words, and characters of text. SVM is a technique often used in handwriting analysis algorithms. SVM as a classifier was applied to analyze various handwriting features like the margin, word spacing, line skew, letter size, sharpness, text density, speed, slant, and regularity of writing, which are extracted from collected handwriting sample images of different individuals [9; 25]. KNN is a simple classifier that computes the distance between the feature vectors. Algorithms based on KNN were used in [26] to improve the feature extraction from handwriting, such as the margin, the bar on the letter t, and the baseline. In [9], an MLP is used to determine a Big Five personality model, and in [27], LSTM, RNN, and GRU are used for writer identification. The paper [28] presents the use of convolutional neural networks for determining the profession using handwriting. The present work analyzes handwriting data collected from the writers from different professions and classifies them based on the top features that characterize their profession. Collectively, the studies to date illustrate the multifaceted nature of handwriting analysis in relation to personality traits. These studies have highlighted a number of problems with traditional psychological methods, problems that can be solved using artificial intelligence in handwriting analysis:  
* Subjectivity of traditional methods--The subjective interpretations and questionnaires used by psychologists as well as the variable level of expertise can influence the results of psychological analyses. * Reduced scalability--Traditional handwriting analysis methods are time-consuming, limiting the scalability of psychological analyses. * Intrusive methods--The use of intrusive psychological analysis methods can influence the authenticity of the subjects' responses.