A combined crisp and fuzzy approach for handwriting analysis

Abstract

This paper presents an off-line writer-independent handwriting analysis system which utilizes both classical crisp and fuzzy methodologies to output possible personality traits of the writer. The design deploys an analytical handwriting analysis approach based on two primitives, the baseline and the slant angle of the characters. The objective of the design strategy is to present a group of parameters for handwriting analysis based on the text. These parameters allow for the classification of writing into different categories which could be used as a preliminary step for outputting the personality traits of the writer. Two parameters, the baseline and the slant-angle, are the inputs to a rule-base which outputs the personality trait category. The evaluation of the baseline is uon-fnzzy (crisp) whereas the evaluation of the slant-angle utilizes the fuzzy paradigm.The approach is based on a combination of classical geometric arithmetic evaluation and fuzzy control designs. For determination of the base line angle two methodologies are explored: the geometric-features based segmentation method and B method based on biologically inspired generation theories or the low pass fdtering method. We utilize the geometric features evaluation for the baseline extraction since it proves more robust with respect to the variations of the handwriting in an off-line environment.For determination of the slant type a fuzzy technique is adopted to determine the contributions of the slant-type angle to each of the five variations of the slant-type categories. The uncertainties in the system model are expressed by fuzzyvalued model parameters with their membership functions derived from experimental data. In total five variations of slant type are considered. These include extreme left, controlled left, vertical, controlled right and extreme right.Fifteen personality traits PTl -PT15 were identified and sets of rules formulation were created, (e.g., IfInputl is "level" and "Input2" is "Controlled Left" then Output is PTx.)The proposed approach takes advantage of two differing methodologies that have clear outputs to evaluate two attributes of handwriting. The outputs are utilized to determine a personality trait The system can he further enbaneed by including more parameters such as size of letters, spacing between letters and other attributes of bandwriting as part of the inputs for trait determination.

Writer Identification on Multi-Script Handwritten Using Optimum Features

“…This is demonstrated by Figure 2 The proposed method concentrates on the important writing areas that provides the most important information on the basis of the foundation of this study and the theoretical work contributed by the handwriting analysis experts which contends that ending-strokes is deemed to be the most significant handwriting parts as they can contribute in predicting personality traits. Figures 2, 3 support this theory of the handwriting experts [19,20] which serve the main concern of this study. The significant of the proposed method is to characterize the handwriting based on the Ending strokes not like previous studies which are employed all the handwriting text to represent the writer.…”

Automatic Emotion Recognition through Handwriting Analysis: A Review

“…It reveals the writer's attitude about reaching his goals and the kind of mental energy he applies to various situations [14]. There are four most common baselines namely Ascending, Descending, Straight and Wavy Baseline whose significant emotional characteristics are described in the following table [6], [10], [30]. …”

Personality Analysis Through Handwriting

“…The literature for the personality analysis through handwriting includes, analysis based on baselines and slants using a combined crisp and fuzzy approach [12], analysis generated through artificial neural networks based on the pen pressure variations in writings, the positioning of the't' bars and the baselines [13]. Another system trained a SVM to predict the behaviour of the writer based on slant of letters and words, baseline, pen pressure, spacing between letters, and words [14].Another method was proposed for behavioural prediction of a person through automated handwriting analysis, it identified the psychological traits namely baseline, number of breaks, margins, speed of writing, and spacing between the words, this analysis was done with the help of image processing in MATLAB [15].…”