# Detection of Personality Traits Using Handwriting and Deep Learning
## 1 Introduction
* The Minnesota Multiphasic Personality Inventory--one of the most widely used and validated psychological instruments for assessing personality and identifying psychological disorders [12]. * Myers-Briggs type indicator (MBTI)--classifies personality into 16 types based on four dimensions: Introversion (I) vs. Extraversion (E), Sensing (S) vs. Intuitive (N), Thinking (T) vs. Feeling (F), and Judging (J) vs. Perceiving (P) [13]. The Myers-Briggs type indicator is a widely used personality assessment tool, especially by individuals and organizations. The MBTI is widely recognized and used in corporate settings, self-help literature, and pop culture, making it more relatable to the general public. The MBTI categorizes people into 16 distinct personality types (e.g., INFJ, ENTP), which are easy to grasp and remember. This simplicity makes it appealing for non-experts. The MBTI focuses on identifying strengths and preferences rather than pathologizing or highlighting negative traits. This makes it more appealing for personal development and team-building exercises. Also, unlike some models that include traits like neuroticism (Big Five) or honesty-humility (HEXACO), the MBTI avoids labeling traits as \"good\" or \"bad\", which can feel less threatening to individuals. It is often used in workplaces to improve team dynamics, communication, and collaboration. Its intuitive framework helps people understand differences in how others think and behave. Many people find the MBTI helpful for exploring career paths that align with their personality type, even if the scientific validity is limited. The MBTI prompts individuals to reflect on their preferences (e.g., introversion vs. extroversion, thinking vs. feeling), which can be a valuable exercise in self-awareness. The MBTI has been around since the mid-20 th century and has been integrated into many organizational and educational systems. Its historical presence gives it a sense of legitimacy, even if it lacks scientific rigor. Expertise and specialized training are typically required to accurately determine handwriting characteristics and to establish the appropriate personality traits. However, the advent of artificial intelligence algorithms, particularly deep neural networks, has enabled the development of highly accurate data classification applications. In the field of image analysis, convolutional neural networks have yielded the best results. In this paper, using convolutional neural networks, an application was developed to automate the extraction of main features from handwritten texts and their correlation with the main personality traits using the Myers-Briggs model. Convolutional neural networks are a popular choice for image information extraction because they can automatically learn and extract relevant features from images without the need for human intervention to manually define these features. CNNs are able to detect objects or features regardless of their position in the image. This is due to the use of convolutional filters that apply the same operation to the entire image. The main contributions of this study are as follows:  
-  Automatic assessment of Myers-Briggs personality indicators. -  Objective determination of personality traits. -  Identification of the main features of handwriting. -  Integration of traditional psychological knowledge into machine learning algorithms. The following section presents a series of results in which artificial intelligence algorithms were used to extract hand characteristics and establish personality traits. The following sections present the characteristics analyzed in this study and the connection with the Myers-Briggs indicators.