Personality is a combination of various characteristics and qualities of an individual. It may be affected by the growth and evolution of one’s values, attributes, relationships with the community, personal memories of life events, habits, and skills. Behaviours and decisions of an individual are largely directed by his/her personality. Identification of such a personality trait can be performed based on an individual’s handwriting features. Handwriting may be unique for each person and a person’s nature, behaviour, and certain psychological aspects can be inferred based on it. It is introduced as the field of graphology, also called graphoanalysis, to analyze personality based on handwriting. We perceived that many researchers have worked on personality and/or behaviour identification based on handwriting, however, most of them were limited to a few numbers of features. According to graphology, there is a vast range of features of handwriting strokes which carry psychological characteristics of the writer. In this survey, we present links between handwriting and personality psychology and examine different mechanisms for features extraction to predict a writer’s personality. Psychologically supported handwriting features help to understand personality traits. The paper relates these features and encourages the use of computer-based graphology for personality prediction. It also discusses applications of graphology in various fields.

Survey on handwriting-based personality trait identification

The lifestyle of humans has modified since digital age where everything may be handled with a tip of the finger, however all those luxuries might return at a value of security or fraud where masking one's identity with a faux one is possible that on the opposite hand isn't possible during a case with handwriting. Handwriting is exclusive to each person like a fingerprint is exclusive to each person. Someone can imitate another person’s handwriting for less than a few words creating it distinctive. Handwriting tells about the character of the person as writing is coupled with brain and it subconsciously leaves a path concerning the temperament attribute like optimistic, pessimistic, balanced, shy, etc., which might be detected. Various forms of handwriting styles are taken into thought like slope or angle of the sentence, number of words in a region, left or right slant of the sentence, etc. The complete system evaluates the handwriting samples based on the above-mentioned handwriting styles and it is divided into four modules with the primary module being the input where the image of handwritten text is taken from the user that is followed by image pre-processing that removes noise and sharpens the contrast of the image for better results, that is then passed to the Convolutional Neural Network (CNN) that analyses the input image with the CNN model which is created by performing CNN on the training dataset and labels the input image accordingly and the last module is the output where the labeled images from the previous module is used to find out the percentage of various traits present in the handwriting sample of the subject.

Personality Prediction based on Handwriting using Machine Learning

Although recognition works on mathematical expressions have been explored for four decades, the current literature and trends are varied and frequently influenced by distinct emerging methods and technology. This situation instigates the necessity of an organized review to provide heedful insight into research trends and patterns currently prevailing in the domain of mathematical expression recognition (MER). To identify and associate (semantic mapping) the leading research zones, core research areas, and research trends steering in the MER domain. Identifying prominent recognition models based on extracted research areas. To develop the development chart from extracted research trends for directing the future works in this direction A manual and automatic search has been performed across the reputed digital libraries for corpus formation. The formulated corpus is used for topic modeling, and Latent Dirichlet Allocation is deployed for information modeling for achieving defined objectives. The corpus of 325 research papers published from 1967 to 2021 has been processed using LDA. The five major research areas and ten research trends are identified. Leading research area is “Segmentation and Classification Procedures”, and the trend with the highest related publications is “Contextual and Graph-based recognition”. “Attention and Deep Networks” has emerged as the newborn trend, and the identified newborn, young, and matured trends impetrate more exploration from the MER research community. 

Recent trends in mathematical expressions recognition: An LDA-based analysis

Handwriting analysis has wide scopes include recruitment, medical diagnosis, forensic, psychology, and human-computer interaction Computerized handwriting analysis makes it easy to recognize human personality and can help graphologists to understand and identify it The features of handwriting use as input to classify a person’s personality traits This paper discusses a pattern recognition point of view, in which different stages are described The stages of study are data collection and pre-processing technique, feature extraction with associated personality characteristics, and the classification model Therefore, the purpose of this paper is to present a review of the methods and their achievements used in various stages of a pattern recognition system

/pdf/automated-handwriting-analysis-based-on-pattern-recognition-4lght13cpi.pdf

Automated handwriting analysis based on pattern recognition: a survey

Classification of personality types based on Arabic handwriting is a challenging task. It has multiple practical application since handwriting is a unique attribute for each person that provides as much differentiation as fingerprints. Accurate analysis of handwritten documents is useful in diverse areas including human resource management, criminal justice, forensics, security, archaeology, and countless other spheres of life. Writer identification is a big challenge mostly due to a limited human capability for observing and recognizing different styles of writing. Arabic is a language used by millions of people. Recognition of handwritten Arabic characters would, therefore, be of tremendous help in various sectors like mail sorting, verification of checks, etc. even in countries where Arabic is used only occasionally. For this purpose, we collected around 160 samples of Arabic handwriting from 83 different writers, where every writer wrote the same paragraph in both normal and fast writing. Carl Jung’s and Isabel Briggs Myers’ personality type theory with 16 different personality type was used for classifying the writers. The writers were asked to complete the personality test that contains 64 questions related to Jung-Myers’ typology. Specific features of Arabic language handwriting were used to match each written document with a personality type of the writer based on a set of classification techniques. We used four different machine learning classification algorithms with 4-fold cross validation technique. We conducted two experiments and achieved a maximum accuracy of 68.67%.

Towards Personality Classification Through Arabic Handwriting Analysis

Graphology is an ancient science which uses different attributes of handwriting to analyze the person's personality traits Features like the size of one's handwriting, the slant et al helps in identifying the particular trait associated with the subject Professional handwriting analysts or Graphologists are engaged in hiring recruitments so that a suitable candidate is preferred conforming to the job requirements In this work, we implement a system which analyses these features and gives a detailed analysis employing the principles of Graphology Extraction of six different handwriting features has been proposed and implemented Image Processing was used for feature extraction using MATLAB

Automated handwriting analysis system using principles of graphology and image processing

Personality of an individual can be analyzed by their handwriting. Handwriting analysis can quickly reveal such factors as one’s character, emotions, intellect, creativity, social adjustment, your desires, fears, weaknesses, strengths, and sexual appetite among others. Features like the size of one’s handwriting, the slant, and others help in identifying the particular trait associated with the subject. A handwriting analysis report can help you gain insights into one’s own strengths and weaknesses. In this work, we suggest an algorithm to extract one of the features used in graphology, i.e., Tittle over letter i. Image Processing was used for feature extraction using MATLAB.

Anamika Sen

Papers

Automated handwriting analysis system using principles of graphology and image processing

An Algorithm to Extract Handwriting Feature for Personality Analysis