How can handwriting determine personality

From physiological conditions like high blood pressure and schizophrenia to personality traits like dominance and aggression: if you can write by hand, graphologists can analyze you. Everything from the size of your letters to how closely you space words can reveal intricate details of your personality. Here are some of the most common handwriting features you can study and learn more about your personality type:.

Compared to a standard lined sheet of paper, if you write with tiny letters that do not reach the top line, you are likely to have a timid and introverted personality. If you write with large letters that go over the top line, you are likely to be the opposite: outgoing, confident, and attention-seeking.

Metrics details. We propose the first non-invasive three-layer architecture in literature based on neural networks that aims to determine the Big Five personality traits of an individual by analyzing offline handwriting. We also present the first database in literature that links the Big Five personality type with the handwriting features collected from subjects containing both predefined and random texts.

Testing our novel architecture on this database, we show that the predefined texts add more value if enforced on writers in the training stage, offering accuracies of Overall, our approach offers the highest accuracy compared with other state-of-the-art methods and results are computed in maximum 90 s, making the approach faster than the questionnaire or psychological interviews currently used for determining the Big Five personality traits.

Our research also shows there are relationships between specific handwriting features and prediction with high accuracy of specific personality traits and this can be further exploited for improving, even more, the prediction accuracy of the proposed architecture.

Handwriting has been used for centuries as a way of communication and expression for humans, but only recently its links to the brain activity and the psychological aspects of humans have been studied. The psychological study of handwriting with the purpose of determining the personality traits, psychological states, temperament, or the behavior of the writer is called graphology and is still a debatable domain as it lacks a standard, most of the handwriting interpretations being done subjectively by trained graphologists.

However, there have been various research papers showing the link between handwriting and neurological aspects of humans, one such study being the one of Plamondon [ 1 ], where it was shown that the brain forms characters based on habits of writers and each neurological brain pattern forms a distinctive neuromuscular movement which is similar for individuals with the same type of personality.

In the current paper, we aim to build the first architecture in literature that is able to automatically analyze a set of handwriting features and evaluate the personality of the writer using the Five-Factor Model FFM. To test this architecture, we propose the first database that links the FMM personality traits to handwriting features, which is a novel aspect of this research paper.

We show that our proposed system offers the highest accuracy compared to other state-of-the-art methods as well as share our findings regarding the relationship between several handwriting features and specific personality traits that can be further exploited to improve, even more, the accuracy of such a system. In the following section, we present the state-of-the-art in the area of handwriting analysis, focusing on papers related to predicting the psychological traits of individuals.

We continue in the subsequent section with describing the two models used FMM and graphology analysis followed by a detailed presentation of the three-layer architecture, as well as the classifiers and the structure of the neural network used. Finally, we detail the experimental results and share our findings and conclusions on the results obtained.

As mentioned previously, currently, there is no standard developed in predicting behavior based on handwriting, the majority of graphological analysis being done by specialized graphologists.

However, research was conducted in the area of computer science which aimed to create such systems in order to recognize the behavior from handwriting in an easier way and also to standardize the graphological analysis. In the next paragraphs, we present the state-of-the-art in this area as well as several studies which made use of handwriting to determine the psychological traits or mental status of individuals.

Behnam Fallah and Hassan Khotanlou describe in [ 5 ] a research with a similar purpose as the one conducted in this paper, aiming to determine the personality of an individual by studying handwriting. The Minnesota Multiphasic Personality Inventory MMPI is used for training their system and a Hidden Markov Model HMM is employed for classifying the properties related to the target writer, while a neural network NN approach is used for classifying the properties which are not writer-related.

The handwriting image is analyzed by these classifiers and compared with the patterns from the database, the output being provided in the form of the personality of the writer on the MMPI scale. Similarly, in [ 4 ], an instrument for behavioral analysis is described with the task of predicting personality traits from handwriting.

The work of Chen and Tao [ 6 ] also provides an interesting exploratory study where they use combinations of Support Vector Machine SVM , AdaBoost, and k-nearest neighbors k-NN classifiers for each of the seven personality dimensions in order to analyze a unique set of handwriting features.

Their results are promising with accuracies ranging from Although not aiming for personality traits, Siddiqi et al. A set of features is extracted from their writing samples, and artificial neural networks ANNs and Support Vector Machines SVMs are used to discriminate between the writing of a male and that of a female.

The handwriting features employed are slant, curvature, texture, and legibility, computed in both local and global features. Similarly, in [ 8 ], it is proposed a way to describe handwritings based on geometric features which are combined using random forest algorithms and kernel discriminant analysis.

The system is able to predict gender with Another interesting research is the one conducted by Gil Luria and Sara Rosenblum [ 9 ] which uses handwriting behavior in order to determine the characteristics of both low and high mental workloads. They asked 56 participants to write three arithmetic progressions of different difficulties on a digitizer, and differences are seen in temporal, spatial as well as angular velocity spaces, but less in the pressure space.

Using data reduction, they identify three clusters of handwriting types and conclude that handwriting behavior is affected by the mental workload. Zaarour et al. Similarly, Sudirman et al. Researchers in [ 12 ] present a system tasked with decreasing the time for job candidate selection in the pre-employment stage using automatic personality screening based on visual, audio, and lexical cues. The experimental results show promising results in terms of performance on first impression database.

Another direction for many studies involving handwriting analysis is the detection of deceit. Luria et al. As current ways of determining deception are invasive and do not comply with a clinician-patient relationship, such an approach of using the handwriting as a tool is attractive from research perspectives. After this first step, the deceptive and truthful writings of all the subjects are compared and used to divide the subjects into three groups according to their handwriting profiles.

It is found that the deceptive writing takes longer to write and is broader and the two types of writings show significant differences in both spatial and temporal vectors.

In [ 14 ], similar research is conducted, based on the same assumption that for people it is easier to tell the truth than to lie; hence, we need to see changes in both velocity and temporal spaces when analyzing the handwriting features. Conducted in 11 languages, this research demonstrates the same point as in [ 13 ], with the specific purpose of helping managers pinpoint sudden emotional changes and decode handwritten messages to reveal the true meaning of those messages as well as detect lies.

Besides detecting deceit, the handwriting is also used for predicting physical diseases. The study described in [ 17 ] is another research analyzing the link between the handwriting and children with autism spectrum disorder ASD , knowing the fact that children with ASD have several weaknesses in handwriting.

Boys aged 8—12 years and diagnosed with ASD were asked to take a digitized task in order to determine the handwriting performance using advanced descriptive methods.

The study shows moderate to large links between handwriting performance and attention, ASD symptoms and motor proficiency, providing a relationship between handwriting and the ASD symptoms in terms of severity, attention, and motor behaviors. Since handwriting analysis is a complex task requiring multiple techniques in order to analyze the multitude of handwriting features, there is a wide range of methods typically employed.

For offline handwriting analysis, the normalization of the handwritten sample is the first step in order to ensure any possible noise is filtered out. As part of normalization phase, methods for removing the background noise morphological approaches or Boolean filters are typically used [ 18 ] , sharpening Laplace filters, Gradient masking or unsharp masking [ 19 ] , and contrast enhancement unsharp mask filters [ 20 ] are essential for ensuring the analysis of the handwriting is done with high accuracy.

Also, as the contour of the written letters is essential for this task, methods for contour smoothing also need to be used, the most common ones being the local weighted averaging methods [ 21 ].

After all these processing steps are applied to the handwritten sample, the image needs to be compressed and converted to greyscale and different types of thresholding techniques can be employed for this step [ 22 ].

Post-compression, the written text needs to be delimited through page segmentation methods where techniques for examining the foreground and background regions are employed, the most common one being the white space rectangles segmentation [ 23 ].

One of the most challenging tasks is the one of segmenting the handwritten image into text lines and words. For this, the Vertical Projection Profile [ 24 ] method has shown the most promising results and this is the one that we use in this paper for both row and word segmentation. Regarding feature classification, different classifiers are used successfully for each of the handwriting features.

In the following sections, we present in detail the classifiers used for each of the handwriting features analyzed in the current paper. With all these in mind, the current research proposes a novel non-invasive neural network-based architecture for predicting the Big Five personality traits of a subject by only analyzing handwriting. This system would serve as an attractive alternative to the extensive questionnaire typically used to assess the FMM personality traits and which is usually cumbersome and non-practical, as well as avoid the use of invasive sensors.

We focus our attention on handwriting because it is an activity familiar to almost everyone and can be acquired fast and often. As mentioned in the previous section, our research is proposing a novel non-invasive neural network-based architecture for predicting the Big Five personality traits of an individual solely based on handwriting.

We detail both these instruments in the next subsections. Big Five Five-Factor Model [ 25 ] is a well-known model for describing the personality of an individual. It is based on five basic personality traits which are grouped in sub-factors, as follows:. Openness to Experience : refers to people who can easily express their emotions and have a desire for adventure, appreciation for art, and out-of-the-box ideas. Typically, on this scale, people are rated based on the dichotomy: consistent vs.

Conscientiousness : refers to people who are dependable, have a predilection towards behaviors which are carefully planned, and are oriented towards results and achievements. On this scale, people are rated based on the dichotomy: organized vs. On this scale, people are rated on the dichotomy: outgoing vs. Agreeableness : refers to people who have a tendency to be compassionate instead of suspicious, as well as helpful, and tempered.

On this scale, people are rated based on the dichotomy: compassionate vs. Neuroticism: refers to people who lack emotional stability and control and tend to experience negative emotions easily, such as anger and anxiety, as well as a vulnerability to depression. On this scale, people are rated based on the dichotomy: nervous vs.

For example, if you write large letters, it could mean you are people oriented, whereas small letters could mean you are introverted. Business Insider spoke to master graphologist, Kathi McKnight, who analyzes handwriting for personality traits, to figure out what these details in your handwriting mean.

Size of letters and words. Upper zone. Lower zone. Connections of letters. Schizophrenics tend to have writing that switches direction in the way that it slants, between left and right, and this is supposedly a sign of a person 'not having continual contact with reality'. One of the symptoms of Parkinson's Disease is very small cramped handwriting, known as micrographia.

Handwriting with heavy pressure is also a sign of high energy levels, whereas light pressure is a sign of tiredness. In other tests, writing the capital letter 'I' much larger than other capitals is usually written by someone who is arrogant and have a high opinion of themselves. Writing that changes dramatically over the course of a text is symbolic of lying. The views expressed in the contents above are those of our users and do not necessarily reflect the views of MailOnline.

Argos AO. Privacy Policy Feedback. What does your handwriting say about you? Study finds more than 5, personality traits are linked to how we write People who write letters close together are intrusive and crowd others Illegible signatures mean a person is private and difficult to understand Words or sentences that vary to the rest of the text suggest it is a lie Schizophrenia and high blood pressure can also be identified from writing By Victoria Woollaston Published: GMT, 29 July Updated: GMT, 29 July e-mail View comments.

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