International Journal of Computer Applications (0975 – 8887)
Volume 130 – No.15, November2015

Handwriting Analysis for Detection of Personality Traits
using Machine Learning Approach
Prachi Joshi

Aayush Agarwal

Ajinkya Dhavale

Associate Professor
Department of Computer
Engineering
MIT College of Engineering
Pune, India

Department of Computer
Engineering
MIT College of Engineering
Pune, India

Department of Computer
Engineering
MIT College of Engineering
Pune, India

Shreya Kodolikar

Rajani Suryavanshi

Department of Computer Engineering
MIT College of Engineering
Pune, India

Department of Computer Engineering
MIT College of Engineering
Pune, India

ABSTRACT
Among all the unique characteristics of a human being,
handwriting carries the richest information to gain the insights
into the physical, mental and emotional state of the writer.
Graphology is the art of studying and analysing handwriting, ascientific method used to determine a person‟s personality by
evaluating various features from the handwriting. The prime
features of handwriting such as the page margins, the slant of
the alphabets, the baseline etc. can tell a lot about theindividual. To make this method more efficient and reliable,
introduction of machines to perform the feature extraction andmapping to various personality traits can be done. This
compliments the graphologists, and also increases the speedof analysing handwritten samples. Various approaches can beused for this type of computer aided graphology. In this paper,
a novel approach of machine learning technique to implement
the automated handwriting analysis tool is discussed.

Keywords
Graphology, Personality Traits, Handwriting,
Extraction, Margins, Baselines, T-bar.

Feature1. INTRODUCTION
Handwriting also termed as brain-writing is a useful measurein identifying the characteristic personality traits of anindividual. Handwriting analysts also known as graphologists
can examine an individual‟s handwriting to predict thepersonality traits of the writer. Automated handwriting
analysis can be used to examine personal traits of candidates
during interviews accurately as the accuracy of an analyst
highly depends on his skill set. Also hiring a graphologist to
analyze hundreds of samples for recruitment purpose will betime consuming and not be feasible economically.
This work discusses about a method for analysing real world
handwritten text samples with the aid of technology. The
analysis is done for specific features of the sample fordetermining various characteristic behavioural traits of theperson. Various parameters of the handwritten sample like
Margin, Baseline, T-bar and Slant will be taken into
consideration to determine corresponding traits [1]. The
proposed tool will compliment the graphologists to increase
their speed and efficiency in the analysis process. Machinelearning approach like KNN with incremental learning, will
be implemented to improve the efficiency of the tool.

2. LITERATURE SURVEY
The study of handwriting is quite an old concept tracing backto the seventeenth century. The first book to document thesemethods was written almost 400 years ago by Camilo Baldi.
Known as the father of graphology, Camilo Baldi, who was an
Italian doctor of medicine and philosophy, performed
systematic observations on handwriting samples in the year
1622. Since then, very detailed and extensive studies havebeen performed in this field. There are more than 2200
documented studies of handwriting analysis till date. [2]A study conducted by the American Psychological
Association's annual convention acknowledges that the use ofcomputer technology in the field of handwriting analysis canbe considered as a reliable tool for determining various traits
like honesty, emotional stability, substance abuse risk andjudgment.
A paper published in the year 1995 at the SUNY, Buffalo by
Prof S. N. Shrihari and two others from the Center of
Excellence for document analysis and recognition gave a lotof insights which led to the progress in the field of Computer
Aided Graphology [3]. This paper focuses only at the prime
features of a page of handwritten sample that are pagemargins, line spacing, line direction, slant and zone ratios. To
do this, the methodology used is scanning, preprocessing,feature extraction, analysis and finally, trait determination.
This system was mainly designed to prove the validity of thegraphology rules that were applied in the implementation of
the system. This paper restricts its scope to macro analysis of
the handwriting sample. There are no micro features likealphabet, loops etc. taken into consideration.
Another paper discusses about computer aided graphology
which intends to reduce the human intervention needed toperform trained analysis on the handwriting input sample. The
features like margin, baseline, size and zones have been
extracted through image processing and an approximate
analysis of the personality trait has been given. These traits
are then mapped to existing theories to determine a final
personality type and generate a report for the same. This work

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International Journal of Computer Applications (0975 – 8887)
Volume 130 – No.15, November2015
includes limited physical features of handwriting. Additional
perspective can be provided by using approaches like Natural
Language Processing which can improve the efficiency of thesystem [4].
A paper based on artificial neural network explores theimplementation of a machine learning approach in the field ofhandwriting analysis. This paper proposed a method to predictthe personality traits of a person by analyzing the baseline,
pen pressure and the letter „t‟ as found in the individual‟shandwriting sample. These extracted features are then given
as an input to the artificial neural network which in turn gives
output as personality trait to the user. The future work
discussed in this paper are, including more features of thehandwriting like the size of the letters and the margins as
inputs for personality trait determination to improve thesystem output[5].

analogous target vectors are used to train a network until it
can approximate function is determined which can associate
input vectors to output vectors, or classify input vectors in an
appropriate way defined by default [5]. Fig 3 [8] shows aneural network active node. This is a flow diagram of theactive nodes used in the hidden network. Each input ismultiplied by weight (the wN values), and then summed. This
produces a single value that is passed through an “s” shaped
nonlinear function called sigmoid.

The various algorithms and techniques used for the analysis
have been discussed below:

2.1 Polygonalization
Polygonalization is a method of subdividing the plane into
polygons. This is the main technique used to find the slant of
the baseline. In this technique of Polygonalization, a closed
polygon is drawn around a single line of the scanned
handwriting sample. The slope of the polygon in Fig. 1 isfound using the coordinates of the polygon. This slope
corresponds to the slope of the baseline. [5]Fig. 1: Polygonalization

2.2 Thresholding Algorithm
Image thresholding is an uncomplicated and effective way ofpartitioning. This image analysis algorithm is a type of image
segmentation that segments objects by transforming grayscale
images into binary images. Image thresholding is mosteffective in images with high levels of contrast such likehandwritten sample on white sheet using a black pen [6].

2.3 Template Matching
Template matching is a technique in image processing used
for finding small parts of an image which match a given
image that is template image. From the scanned handwritten
sample, individual lines are isolated and individual characters
are identified. Finally, the identified characters are comparedwith all the template images by correlation and a match isfound. [7]Fig. 3: Artificial Neural Network

3. PARAMETERS FOR ANALYSINGHANDWRITING
Graphologists are the handwriting analysts who identify thecharacteristics traits of a person merely by examining thehandwritten samples of the individual. As this is a manual
process, the skilfulness of the examiner defines the accuracy.
However this is a time consuming and costly affair solely
because of the human intervention. Thus, in the proposed
methodology the focus would be on the development of acomputer aided tool with minimum human intervention thatwould be able to predict the characteristic traits of a person
intelligently. Baseline, writing pressure, spacing between
letters, words and lines, size of letters, strokes connecting theletters, width of margins, starting strokes, ending-strokes,
slant of word etc. are the most common parameters that help
in identifying the personality traits of an individual throughhandwriting analysis. This paper would be focusing on thefollowing four parameters: Baseline, Letter-slant, Height of
the T-bar and Width of Margins.

3.1 Baseline
The baseline is the feature which reveals a great deal of
information as far as the personality of the writer isconcerned. Baseline in a person's handwriting is the imaginaryline along which the writer aligns the bottoms of the middle
zone letters, when asked to write on a blank paper. Slanting
downwards, rising upwards and level are the three mostcommon baselines found in handwriting [5]. Different
personality traits are associated with each of the above typesof baselines. These characteristic traits are mentioned in thetable below.
Table 1: Baseline Characteristics

Fig. 2:Template Matching

Baselines

Corresponding traits

Ascending(rising upwards)

Optimistic

Descending(slanting downwards)

Pessimistic

Level(Straight)

Balanced

2.4 Artificial Neural Network
When large sample of data is analysed, different combinationsof traits are derived. These combinations of traits are analyzed
to predict the personality trait using artificial neural network.
Back propagation is the abstraction of the Widrow-Hofflearning rule to multiple-layer networks and nonlinear
differentiable transfer functions. Input vectors and the41International Journal of Computer Applications (0975 – 8887)
Volume 130 – No.15, November2015
paper would be specifically focusing on the height of the tbar. The t-bar can cut the stem of the letter 't' at different
positions like the lower-portion, mid-portion, upper-portion of
the stem or not cut the stem at all. Different characteristics
traits of a writer can be identified depending on where thewriter crosses the t-bar [11]. These personality traits are
mentioned in the table below.
Table 3: ’t’ Bar Characteristics
Position of t- Corresponding trait
bar

Fig. 4: Types of Baseline
The technique that would be used to determine baseline, inthis paper is polygonolization. In this method the smallest
possible polygon around the line to be examined is drawn.
This polygon should cover every point on the line. The slopeof various sides of this polygon will help us identify thebaseline.

3.2 Letter Slant
The slant of the letter is used to understand whether anindividual‟s handwriting is inclined towards the right or
towards the left or is it vertical. It has been observed that,around 77 percent of individuals write with a right slant, 15
percent with left slant and remaining 8 percent write vertically[9]. The slant of an handwriting is in relation to an individual's
emotional direction and degree of sentimental control. The
writer's connection between the inner and outer world isindicated by means of the slant of the letters.

Crossed very High self-esteem: This reveals confidence,high(but not ambition, and the ability to plan ahead, highabove
the goals, high personal expectations and an
stem)overall good self-image.
Crossed just Moderate self-esteem: It indicates a practicalabove
the and successful person.
middle zone
Crossed very Low self-esteem: This person fears failure
low on the and resists change. He is rarely successfulstemenough in his own eyes despite his
accomplishments and resists change.
Crossed above Dreamer: This person's goals and dreams
the stemlose touch with reality.

Table 2: Slant Characteristics
Slant

Corresponding trait

Extremeleft

Fear of the future, defensive, early rejection.

Extremeright

Lack of self-control, impulsive, unrestrained,
intense, very expressive, low frustration tolerance.

Left

Reflective, independent, not sympathetic,
difficulty in adapting and expressing emotions.

Right

Expressive, confidence in convictions, freedom ofthought, extrovert, future orientation.

VerticalHead controls over heart, independent emotional
nature, and works independently.

To determine the slant, the following method is proposed. In
the grid of every individual letter that would be obtained after
template matching, a line will be drawn between thelowermost and the uppermost points of that letter [10]. Slopeof this line will calculate and similarly it would be calculated
for all other letters. The average of all slopes calculated will
give the slant of letters [7].

3.3 Height of ‘t’ bar

Fig. 5: Types of ‘t’ Bar
Through template matching all the lowercase „t‟ letters will besingled out and compared with the predefined templates [10].
4 templates of letter „t‟ will be predefined each consisting of
t-bar at a height very low on the stem, at mid level on thestem, at very high height on the stem and out of the stem
respectively. The given template will be matched with thepredefined ones using Hamming distance [12]. Hamming
distance will measure the minimum number of substitutions
required to change one into the other. The predefined
template which is achieved with minimum number ofsubstitutions on the input template will be the matching
template.

Another important feature that reveals lot of accurate
information about the writer is the lower-case letter 't'. There
are different ways in which a lowercase 't' can be written. This

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International Journal of Computer Applications (0975 – 8887)
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3.4 Margin
The margin is generally considered to define the layout of thepage. Any individual assumes a particular margin whilewriting on a blank paper. The blank spaces on the left, right,
top and bottom on a page comprises the margin. In this paper,
different types of left and right margins namely- wide right,
wide left, no margin at all and evenly spaced margin will beconsidered. Following are the characteristic personality traits
of individuals associated with the above mentioned types ofmargin [1].
Table 4: Margin Characteristics
MarginOrientation

Corresponding trait

Wide left

Courageous.

Wide right

Avoids future and a reserved person.

No margin

Insecure
andcompletely.

Even margin

Self disciplined and balanced.

devotes

oneself

on the gray scale will be considered as the left margin.
Similarly to obtain the right margin vertical lines will bedrawn from the top right

4. PROPOSED APPROACH
This work aims at obtaining personal characteristic traits ofindividuals especially in the age group of 20-35 years when
they face many interviews.

4.1 Generation of Training Dataset
Sample Training set will be generated using 100 samples ofhandwriting, which will be examined by a professional
Graphologist. Using Image Processing tools the feature
vectors matrix of the same handwriting samples will be
generated. The Feature Vector Matrix is the mathematicalrepresentation of the handwritten text in form of feature
vectors. Based on the characteristics corresponding toparticular trait, as identified by the graphologist, classes willbe created. Initially, each FV Matrix, created for a particularhandwriting sample, will be manually mapped into its
corresponding class as suggested by the graphologist [13]. In
this way a dataset will be generated that contains individualclasses corresponding to each trait. Later a supervisedmachine learning algorithm would be used for mapping new
samples into previously identified classes.

To obtain the margin, virtual vertical lines will be drawn fromthe top left on the scanned image. The line that would
encounter a good number of pixels with a low numerical value

Fig. 6: Process Flow Diagram for Generation of Training Dataset

4.2 Trait Identification Based on Trained
Data
Once the dataset is trained, it will be easy to identify the traits
corresponding to a new sample of handwriting. The Feature
Vector Matrix for a new handwriting sample will be created
and then its similarity with the trained dataset would be
calculated using the similarity matrix method. K-NN classifier
will be used to identify the class which is most appropriate forthe handwriting sample, based on the similarity matrix. In this.

way the handwriting would be identified in a class that will
map the handwriting to its corresponding trait [14]. After the
results are produced, it would be stored in the trained dataset.
By storing the new sample into the class to which it is mappedwill be able to implement the incremental machine learning,
increasing the accuracy of the future results that will be
produced.

43International Journal of Computer Applications (0975 – 8887)
Volume 130 – No.15, November2015

Fig. 7: Process Flow Diagram for Classification of Traits

4.3 Mathematical Model
1)

Building the training set

For TD1 to TDn € C1 to Cm
where,
n is no. of training documents
m is no of classes(i)

Extract f1 to fy features [Selection based on discussed
parameters](ii) Build FVi for every TDi
where,
FVi = {f1….fy}
2)Classification of unknown sample x
For TD1 to TDn(i)Calculate similarity(TDi,x)(ii)

Determine value of k(iii)

For C1 to Cp obtained from k;
Classify x  Cf | Cf is majority class(iv)

Determine Cf  trait identified for x

5. CONCLUSION
This paper has proposed a methodology to predict theaccurate personality traits of an individual from the features
extracted from handwriting using a machine learning
approach. This paper explores the personality traits revealed
by baseline, margin, slant of the words and height of t-bar of aperson's handwriting. These features will be extracted fromthe handwriting samples into feature vectors which would becompared with an initially trained data set; and then mapped
to the class with corresponding personality trait. The baseline
would be evaluated using the method of Polygonalization
while margin will be calculated using the method of verticalscanning. The height of the t-bar on the stem of the alphabet 't'
and word-slant would be calculated using template matching.
The proposed system can be used as a complementary tool by
the graphologist to improve the accuracy of handwriting
analysis and also make the process fast. It will also assist the
HR/company employer in decision making regarding thesuitability of an employee for the specific job and improving
the retention of an employee. The future work can be toinclude more features from the micro approach of handwriting
analysis like the loops of alphabet 'f' and 'l', gradient,concavity of letters and so on in order to predict moreaccurate results.

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