Graphology research

GRAPHOLOGY RESEARCHES
Prepared by
Buthainah hamdy
3/19/2016 1

Contents
•Introduction
•Applications of graphology
• behavior prediction
• Forensic
• medical diagnosis
•References
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Introduction
•Graphology or Handwriting Analysis is a
scientific method of identifying, evaluating and
understanding personality through the strokes
and patterns revealed by handwriting.
•Professional handwriting examiners called
graphologist.
• It is also used in forensic evidence and to
diagnose disease.
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Applications
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behavior prediction
Forensic
Disease diagnosis

Human behavior
• 1-[2010] International Journal of Computer Applications, Artificial Neural Network for Human
Behavior Prediction through Handwriting Analysis.
• In this paper handwriting analysis is a scientific method of identifying, evaluating and understanding
personality through the strokes and patterns revealed by handwriting.
• The most common features of handwriting to predict personality are
• Baseline
• size of letters
• writing pressure
• connecting strokes
• spacing between letters
• words and lines
• starting strokes
• end-strokes
• word-slant
• speed of handwriting
• width of margins
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Human behavior prediction(cont.)
• In this paper particular features are the method to predict writer personality:
• The baseline
• Writing pressure
• the height of the t-bar on the stem of the letter ”t”.
• The values calculated for these features are the input for ANN(Artificial
Neural Network).
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•
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• the height of the t-bar on the stem of the letter ”t”.
The methodology of identification of certain
traits of the writer through examining
the letter “t” involves template matching
with certain predefined templates.
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• 2-[2010] International Journal of Computer Applications, Handwriting Analysis based on
Segmentation Method for Prediction of Human Personality using Support Vector Machine.
• six main different types of features to identify the personality of the writer:
• size of letters
• slant of letters and words
• baseline
• pen pressure
• spacing between letters and words.
• Segmentation is used to calculate the features from digital handwriting and
is trained to SVM(support vector machine) which outputs the behavior of the
writer.
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• 2-[2010] International Journal of Computer Applications, Handwriting Analysis based on Segmentation
Method for Prediction of Human Personality using Support Vector Machine.
PROPOSED METHODOLOGY
Result and discussion
Training and testing SVM
Feature extraction
Image handwriting segmentation
Image handwriting pre-processing
Image handwriting acquisition
Three main steps: pre-processing,
feature extraction, and classification.
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• Image Handwriting Pre-Processing
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• Image handwriting segmentation and feature extraction
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• Feature extraction
• 1-size of the letters
• 2-Slant of Words and Letters
• To calculate the slant in world and letter we use simple
trigonometric formula:
Right slant left slant vertical slant
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• 3-Baseline
a) Rising
b) Straight
c) Falling
d) Erratic
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• Pen Pressure
• 1-shows the physical and mental levels.
• 2-Heavy pressure indicates commitment and taking things seriously
,Light pressure shows sensitivity to atmosphere and also empathy ‫متعاطف‬to others.
• 3-To measure this simple thresholding technique is used.
• 4-The documents darker than the threshold value “th0”,
the document is heavy pressure else it is light pressure.
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• Spacing between Words
• Classification
• SVM as it gives good result with more accuracy, SVM is time efficient to than neural network
as tested.
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• Classification
The classification can be performed in following three
steps:
1. First, the input features are formulated as input vectors
in some feature space.
2. Map these feature vectors to the higher dimension
feature space using RBF kernel function.
3. Then a division global hyperplane is computed to
separate the feature space optimally to the classes of the
training vector samples.
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• 3-[2011] ARPN Journal of Engineering and Applied Sciences ,DEVELOPMENT OF AN
AUTOMATED HANDWRITING ANALYSIS SYSTEM(AHWAS).
• this paper study a method has been proposed for the behavioral prediction of a person through
automated handwriting analysis system(AHWAS).
• The system can be used in various applications such as:
-1 detection of diseases like Parkinson’s disease ‫رعاش‬ ‫شلل‬ or Alzheimer's disease ‫زهايمر‬ ,cancer
through the variation in features obtained over a period of time
-2 forensic document examination
-3 lie detection.
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• METHODOLOGY
• It has primarily three steps namely
image preprocessing, feature extraction
and prediction.
• The image is stored in JPEG format.
The region of interest is cropped
and the image is the input to AHWAS.
Experimental verification
Behavioral prediction
Trait acquisition
image preprocessing
Camera image of handwriting
sample(Nikon coolpix S610)
Proposed method
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The features identified are:
Size of the letters
Pressure of the writing
Number of breaks
Margins
Baseline
Slant of the handwriting
Spacing between the words
Speed of the writing in the sample
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• FEATURE DETECTION BY AHWAS
• 1-Size of the letters and baseline
• The distance between the two blue lines gives the size of the writing.
• The distance between the two lines with circles gives the average size of the entire
sentence.
• The baseline is an imaginary line on
which the bottom of the middle zone letters
aligns.
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• 2-Pressure of the writing
• the pressure of the writing in the image can be
determined by the width of the stroke.
• a line (green) which cuts across the word
is obtained.
The number of black pixels at all intersections
/ the number of intersections
=the pressure of the writing
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• Slant of the writing
• The slope of the line is obtained as an array
and this gives the slant at every point in the word.
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• Breaks in the writing
• Connected or disconnected
• It is determined by blob analysis using a nearest centroid algorithm .
• #Breaks = #blobs(white patches) - #words
• Spacing between the words
the number of pixels between the end of the one word and the start of the next word.
• Margins
• The margins are obtained by the space between the starting point of the sentence and
the edge of the paper or the last point of the sentence and the edge of the paper.
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• Speed of the writing
• To effectively asses speed of the writing we use two algorithms:
• 1-measuring the ink density
• 2-measuring the behavior of stroke length at the last twenty pixels.
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• 4-[2013] International Journal of Image Processing (IJIP), HABIT: Handwritten Analysis
Based Individualistic Traits Prediction.
• This paper aims at implementing an off-line, writer-independent handwriting analysis system
“HABIT”.
• HABIT acts as a tool to predict the personality traits of a writer automatically from
features extracted from a scanned image of the writer’s handwriting sample given as input.
The features include:
• slant of baseline
• pen pressure
• slant of letters
• size of writing.
• Compared to manual analysis, automated handwriting analysis is very fast and accurate in
the prediction of human personality.
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• 4-[2013] International Journal of Image Processing (IJIP), HABIT: Handwritten Analysis Based
Individualistic Traits Prediction.
• SYSTEM DESCRIPTION
• Although human intervention in handwriting
analysis has been effective,
it is costly and prone to fatigue ‫.مجهده‬
• predict the personality traits automatically
with the aid of a computer.
• the aim is to create software that allows
a user to analyze handwriting samples.
• the aim of making the process faster
and more objective.
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4-[2013] International Journal of Image Processing (IJIP), HABIT: Handwritten Analysis Based Individualistic
Traits Prediction.
• SYSTEM DESCRIPTION
• Image Acquisition & Processing.
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Applications
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behavior prediction
Forensic
Disease diagnosis

Forensice
• 1-[2013] Expert Systems with Applications ,Texture-based descriptors for writer
identification and verification.
• the use of texture descriptors to perform writer verification and identification.
• local binary patterns descriptors.(LBP)
• local phase quantization descriptors.(LPQ)
• both LBP- and LPQ-based classifiers are able to surpass previous results reported in the literature for the
verification problem by about 5 percentage points.
• Objectives
• How the framework performs on the problem of writer identification?
• How the dissimilarity-based approach compares to other feature-based
strategies?
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Forensice (CONT.)
• Databases
Datasets
IAM databaseBrazilian forensic letters database(BFL)
315 writers, with three samples
per writer, for a total of 945 images,
Each writer was allowed to use his/her
own pen.
A total of 650 writers have contributed to
the dataset, with 350 writers having only
one page, 300 writers with at least two
pages, and 125 writers with at least
four pages.
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Forensice (CONT.)
• Databases
• BFL database
• In order to validate the main hypothesis of the dissimilarity based approach, we divided the database into two
corpora: one composed of 200 writers for training, and the other of 115 writers for testing.
• IAM database
• is one of the best known and widely used databases in problems such as handwriting recognition and writer
identification.
• 650 writers represented in the database were divided into training and testing.
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Forensice (CONT.)
• 2-The Graphometry Applied to Writer Identification
• Introduction
• the Forensic Document Examiners (FDE) use inscriptions and different markings as clues to identify
a writer.
• the forensic handwriting identification is part of criminology and this analyses provide a great
number of elements that affect a person’s writing.
• the forensic handwriting identification is performed by experts using optical device and/or chemicals
methods.
• the manual features extraction process is tedious ‫ممل‬ and can provide doubts about the writer
identification.
• In addition, different graphologists can extract the same features from a particular document in a
different way.
• Then, the use of semi-automatic identification systems can be useful and helpfully to the experts.
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Forensice (CONT.)
• this paper discussed the efficiency of a graphology feature set which can be
applied to writer identification.
• Database
• Brazilian Forensic Letter Database(20 different writers)
•classifier
• SVM
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Forensice (CONT.)
• Proposed Method
• Consist of two stage
• Training stage
• Test stage
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Forensice (CONT.)
• Overview of the feature extraction process
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Forensice (CONT.)
• 3-[2014] Al Maadeed and Hassaine EURASIP Journal on Image and Video Processing
Automatic, prediction of age, gender, and nationality in offline handwriting.
• introduction
• The classification of handwriting into different categories:
• Age
• Gender
• Nationality
• In forensics, handwriting classification helps investigators focus on a certain
category of writers.
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Forensice (CONT.)
• Database
• the QUWI dataset,
• This dataset contains handwritings of 1,017 writers in both English and Arabic.
• a set of 29 features extracted from the online information and its offline representation
and applied support vector machines(SVM) and Gaussian mixture models(GMM) to perform
the classification.
• In this paper, we propose a new method for the detection of the age range, gender, and
nationality of the writer of a handwritten document.
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Forensice (CONT.)
• features:
• Directions ‫اتجاه‬
• curvatures ‫تقوس‬
• Tortuosity ‫تعرج‬
• chain codes
• edge-based directional
• Classifiers
• random forests classifier
• Kernel discriminant analysis using spectral regression
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Forensice (CONT.)
• General scheme of our method
• Our method consists of two main steps:
feature extraction and classification.
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Forensic(cont.)
• 4-[2012] Somaya Al-Maadeed, Journal of Electrical and Computer Engineering, Text-
Dependent Writer Identification for Arabic Handwriting.
• 1-database of words was assembled and used as a test base.
• 2-features vectors were extracted from writers’ word images.
• Two fundamental concepts
• no two people write exactly alike.
• no one person writes exactly the same way twice.
• The task of writer identification is equivalent
to answering one question: “who wrote this sample?”
• The recognition rate for top ten writers is greater than 90% for certain words
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Handwriting by eight different writers.

Forensic(cont.)
DependentWriter Identification for Arabic Handwriting.
• The identification of writers of handwritten documents has great importance
in the criminal justice system and forensic handwriting analysis.
• Problem
• presents the problem of automatic writer identification using scanned
images of Arabic handwriting.
• Objective
• identify the writer of one, or several, lines of handwritten text.
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Forensic(cont.)
• RelatedWork
• 1-identification of writer as abiometric
• 2-related fields in forensic science, classic forensic handwriting examination
is primarily based upon the knowledge and experience of the forensic
expert.
• 3-According to Ameur Bensefia, handwritten documents generally exhibit
two kinds of use:
• 1-textual content.
• 2-graphical content.
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Forensic(cont.)
• RelatedWork
• 4-system for writer identification using textural features derived from the
grey-level co-occurrence matrix and Gabor filters is described.
• 5-Franke et al. developed a computer system, known as the FISH system,
for retrieving a small set of documents from a larger set.
• 6-system known as CEDAR-FOX has recently been developed by
Srihari et al.
• CEDAR-FOX provides users with three major functionalities:
• 1-document analysis system.
• 2-system for creating a digital library.
• 3-database management system for document retrieval and writer
identification.
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Forensic(cont.)
• System Overview
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Scanned handwriting
preprocessing
Feature extraction
classification

Forensic(cont.)
3/19/2016 47
4-[2012] Somaya Al-Maadeed, Journal of Electrical and Computer Engineering, Text-Dependent
Writer Identification for Arabic Handwriting.
Training and testing phases in the text-dependent writer identification system.

Forensic(cont.)
• Data Set
• 100 writers using the same pen.
• (i) Filling of the forms by 100 people using
the same pen.
• (ii) Scanning the forms using the same
scanner (300 dpi, millions of colors).
• (iii) Cropping and naming each word
image using a program that we created for cropping.
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Forensic(cont.)
• Preprocessing
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Page Scanning
Document Segmentation
Removing Background
Edge Detection
segments the page into words and stores each
word in a separate file.
used Otsu’s method
Sobel edge detection
scanned at a resolution of 200 by 200 pixels.

Forensic(cont.)
• Feature Extraction
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1-The edge-direction distribution
2-Moment Invariants
3-word measurements feature
extraction
4-angle
8-angle
16-angle
12-angle

Forensic(cont.)
• Feature Extraction
• 1-Area.
• 2-length.
• 3-Height.
• 4-Height from the Baseline to the Upper Edge.
• 5-Baseline to the Lower Edge.
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3-word measurements feature
extraction

Applications
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behavior prediction
Forensic
Disease diagnosis

Medical diagnosis
• 1-[2008] Early Cancer Detection through Graphology Analysis
• Introduction
• Handwriting is brain-writing.
• the microscopic characteristics of handwriting strokes become an important
method for examining human neuromuscular development.
• Alfred Kanfer, is considered the pioneer of the graphological neuromuscular
test for determination of groups at high risk for cancer.
• He had an impressive 84-percent accuracy rate in detecting the disease
through handwriting.
• The Kanfer test alone does not determine the presence or absence of
cancer; it determines a factor associated with cancer.
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Medical diagnosis
• [2009] Early Cancer Detection through Graphology Analysis
• 1-The “Heart Tick”
• Graphologists have determined that certain breaks in writing, slight
interruptions in the upstroke and in the downstroke , especially in letters with
loops, can point to heart disease.
• They call this break a “heart tick” and find it particularly in the lower-case h
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Medical diagnosis
• Finding Cancer in Its Early Stages
• Samples of microphotographs of Mrs. B’s handwriting.
• First Sample
• Smooth, continuous flow of movement
• The strokes have an oval shape, the turns
from descending to ascending strokes
are narrow, curved, and show continuity of
movement throughout.
• A regular pattern of heavier (wider and darker)
descending strokes and lighter ascending
strokes prevails throughout the sample.
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Age 28
Age 33
Age 41

Medical diagnosis
• The second sample
• Heavier descending strokes and
lighter ascending strokes is still preserved
• The narrow turns have disappeared.
• The writing spreads out widely
• The strokes are much weaker and highly
unstable‫مستقر‬ ‫غير‬
• Clear interruptions between descending
and ascending strokes are also visible.
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Age 28
Age 33
Age 41

Medical diagnosis
• Third Sample
• Breakdown of every phase of the writing process.
• The strokes are stiff or formless.
• The pressure is uneven, sometimes too heavy,
and in other strokes too light.
• There are clear interruptions between descending
and ascending strokes.
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Age 28
Age 33
Age 41

REFERENCES
1.H. N. Champa and K. R. AnandaKumar, "Artificial Neural Network for Human Behavior
Prediction through Handwriting Analysis," International Journal of Computer Applications,
pp. 36-41, May 2010.
2.S. Prasad, V. K. Singh and A. Sapre, "Handwriting Analysis based on Segmentation Method
for Prediction of Human Personality using Support Vector Machine," International Journal of
Computer Applications, pp. 25-29, Ooctober, 2010.
3.V. Kamath, N. Ramaswamy, N. P. Karanth, V. Desai and S. M. Kulkarni, "Development of an
Automated Handwriting Analysis System," ARPN Journal of Engineering and Applied
Sciences, pp. 135-140, Septemper 2011.
4. Al Maadeed and Hassaine EURASIP Journal on Image and Video Processing 2014,
2014:10, Automatic prediction of age, gender, and nationality in offline handwriting
5.D. Bertolini, L. S. Oliveira, E. Justino and R. Sabourin, "Texture-based descriptors for writer
identification and verification," Expert Systems with Applications, vol. 40, 2013.
6.J. Engel, "Early Cancer Detection through Graphology Analysis," 2009.
7.http://handwriting.qu.edu.qa/dataset/
8.http://tc11.cvc.uab.es/datasets/type/
3/19/2016 58

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