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Automated Arabic Graphology
05/01/2023
Faculty of Computers and Information , Menoufiya University
Presented by
Buthainah Hamdy
05/01/2023 2
Agenda
IntroductionApplicationsHandwriting analysis on-line vs. Off-line.Features for Arabic vs. English writingsResearch PlanReferences
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Introduction
Brain writing
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Introduction(cont.)
Brain writing
Conscious MindControl-WHAT
we write
SUB-Conscious MindControls-HOW we write
Governs our Moods ,feelings , behaviors and
A significant part of our personality.
Act of writing involves Conscious and Sub-conscious mind, Nerves, Muscles and Fingers
The strokes we make while writing , slant , loops , spacing , margins , pressure and many other are takes care of by the subconscious mind.
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Handwriting occurs through the interactions of many structures and circuits in the brain.
When one portion of the brain is damaged, handwriting is affected in a way that reflects the function of that structure or circuit.
Introduction(cont.)
Brain writing
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Graphology is a word originated from Greek language.
The first person that carried out systematic observations on the manner of handwriting was Camillo Baldi in 1622 AD.
2 Greek words
Graphein
(writing) Logos
(science)
Introduction(cont.)
• Graphology is a scientific method of identifying, evaluating
and understanding personality through the strokes and patterns revealed by handwriting.
• It is a study of any graphic movements, such as hand writing, drawings, scribbling and doodles.
• Professional handwriting examiners called graphologist.
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Graphology reveals insights into the mental, physical of the writer.
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Introduction(cont.)
Habits , Likes and Dislikes
Relationship patterns
Intelligence
Your handwriting develops right from childhood, adolescence and adulthood.
Emotions ,Feelings and Temperament
Intuition and Instincts
Creativity and Talents
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Common Features of Graphology
Introduction(cont.)
Size Baseline
Pressure
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Introduction(cont.)
Slant Zones
Speed in writing
And Margins
Spacing between
letters ,words and line
Common Features of Graphology
Introduction(cont.)
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personality Arabic English/التهكمSarcasm
تقدير عدم/الذاتLow self- esteemعالي احترام/للذاتHigh self -esteem
Personality analysis in Arabic and English
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personality Arabic English/اصرارPersistence
/عدوانيةAggressive
فكر / سيولةFluidity of thoughts
Introduction(cont.) Personality analysis in Arabic and English
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personality Arabic Englishمسحوب /عاطفياEmotionally withdrawnمزدوج /الشخصيةDual personality/دبلوماسيdiplomacy
Introduction(cont.) Personality analysis in Arabic and English
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personality Arabic English/مجادلargumentative
وسيطرة /هيمنةdominant
عالي /تركيزconcentration
Introduction(cont.) Personality analysis in Arabic and English
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personality Arabic English/غامضsecretive
/ الكذبlaying
المشاعر /اتزانambivert
Introduction(cont.) Personality analysis in Arabic and English
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Agenda
IntroductionApplicationsHandwriting analysis on-line vs. Off-line.Features for Arabic vs. English writingsResearch PlanReferences
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Applications
Graphology
Personality prediction
Forensic
Diseasesdiagnosis
1-Human behavior(Extraversion)2-Marital compatibility3-Business compatibility
4-RECRUITMENT(Employment
profiling)5-Education6-Lie detector
1-Writer identification2-Investigations3-Age,gender , nationality and handedness4-Forged Signatures
1-Mental diseasesSuicide, Alzheimer,Schizophrenia andDepression analysis
2-physical diseasesHeart, cancer , Hypothyroidism(graves’ diseases) and Parkinson's Disease
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Agenda
IntroductionApplicationsHandwriting analysis on-line vs. Off-line.Features for Arabic vs. English writingsResearch PlanReferences
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Handwriting analysis on-line vs. Off-line.
On-line Off-line Low noise High recognition (Automatic conversion of
text) Written on a special
digitizer or PDA.Elements digital pen or stylus . Touch sensitive surface. Software application.
High noise Low recognition (scanned image) Written on papers
Elements Fountain pen A4 paper Scanner or Digital
camera
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Agenda
IntroductionApplicationsHandwriting analysis on-line vs. Off-line.Features for Arabic vs. English writingsResearch PlanReferences
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PersonalityPrediction
Human behavior
Extraversiondetection
Employmentprofiling
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Human behaviorDatabase Features Classifiers AccuracyMultiple samples Baseline
pen pressureHeight of the T-bar
ANN(Artificial Neural Network).
100 writers (70-80 words) most of them are cursive , few of them are printed
Size of letters.Slant of letters and words.Baseline.pen pressure.Spacing between letters.Spacing between words.
SVM(support vector machine)
30 writer ofAge between(20-24) 100 words
size of lettersslant of letters and wordsbaselinepen pressurespacing between letters and wordsBreaks(connected&disconnected)MarginsSpeed
AHWAS (Automated Handwriting Analysis System)calibrated with manual analysis.
883 writers (404men ,479 women) age from 20 to 30 years
SizeWidth of middle zone lettersSlantSize of marginsThe way of ending the verseAngularityStability of pressure
SVM(SupportVector machine)
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Human behavior
Database Features Classifiers Accuracy50 samples Margins - Baseline
Size - Zonal ratioSlant - SpaceDegree of connection
Myer Briggs dichotomies Based onKeirsey’s temperament sorter.
handwriting samples Slant - sizePressure - word spacingline spacing - Baseline
Least Squares Linear Regression
100 data set for signature and 156 type of 26 characters
Curved start - End StreakShell - middle streaksUnderline - Extreme marginDot structure - SeparateStreaks disconnected
Learning Vector Quantization (LVQ) for letters,
ANN and multi-structure for signature
10 signatures Curved start - End StreakShell - middle streaksUnderline - Extreme marginDot structure - SeparateStreaks disconnected
ANN and multi-structure
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ForensicSignature verificati
on
Writer Identificati
on
Age, Nationality
,Gender and Handednes
srecognition
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ForensicDatabase Features Classifiers Accuracy5,600 signatures (genuine, random and simulated forgeries).
Static features (caliber , proportion , spacing , alignment to baseline)Pseudo-dynamic features (progression ,distribution of pixels, Form , Slant)
HMM (hidden Markov models).
Offline signature
1-QU online signature database
(194 persons)2-ICDAR 2009
data sets
Pressure DistancesAnglesSpeedAngular speeds
Using multiple classifiers1-Random Forest2-logistic regression3-linear regression4-MARS(Multivariate Adaptive Regression Spline)5-Neural Network with (2,5,10) hidden neuron.
online signature verification for both forgeries and disguised signatures
29 writers by 10 sample/writer, 34 image/sample (9860 images)Enlarge to 70 users
2 auxiliary database final vowel "a" final vowel “o“
First group(writer and his/her writing)Skew ,Slant, PressureVowelinfoA,VowelinfoO Second group(written words and writer)Correlation, Length,Union of letters,Thinning area
SVM,NN+MVA(Most Voted Algorithm)
Brazilian forensic letter database(BFL) (315 writers) 945 images
Texture Features:Caliber , ProgressionProportion , PressureEntry/Exit points , SlantGLCM descriptors
SVM(supportVector machine)
dissimilarity representation
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ForensicDatabase Features Classifiers Accuracy(BFL) 315 writers, IAM database 650 writers
texture descriptorlocal binary patterns (LBP)
local phase quantization (LPQ)
SVM(support vector machine)
Brazilian forensic letter database(BFL) (20 writers)
Brazilian forensic letter database(BFL) (200 writers)
Number of linesProportion of black pixelsRight margin position.The lower left margin position.Upper margin positionBottom margin positionHeight of the first word
Axial slant
SVM(support vector machine)
lAM English handwriting dataset(657 different writers )
DirectionsCurvatures TortuosityChain codeEdge based directional
Random forest
lAM English handwriting dataset
Multi-scale Local Binary Patterns Histogram texture features(MLBPH)
Edge-hinge distribution
Spectral regression(SR-KDA) for dimensionality reduction , K-nearest neighbor classifier(K-NN)
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Diseasesdiagnosis
Cancer and
Heart tick
Graves’ diseases
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Cancer and Heart tick(manual analysis)
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. (En) [19]1-The “Heart Tick”
[2008] Joel Engel , Early Cancer Detection through Graphology Analysis.
Variations of normal handwriting
Down Strokes
Up Strokes
Earlier detecting cancer(cont.) Finding Cancer in Its Early Stages Samples of microphotographs of Mrs. B’s handwriting.
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28
Age 28
Age 33
Age 40
First Sample
Second Sample
Third Sample
Smooth, continuous flow of movement
The writing spreads out widely
clear interruptions between descending and ascending strokes
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Graves’ Disease(Manual analysis)Objectives: Evaluate handwriting characteristics before and after
therapy for hyperthyroid Graves’ disease (GD).(En)[20]Database Features Classifier22patients (15 women, 7 men) with untreated GD(median age: 44 years; range: 20–70 years)
write slandered text before and 12 months after euthyroid
size of letters(mm) distance between
letters width of letters distance between words extension of
letters(assessed in the letters l, t, g, and p)
angles(The presence of the letters a, d, g, and q)
groove depth
Stereoscopic microscope
Magnifying glass.
Giampaolo Papi,1,2 Cristina Botti,3 Salvatore Maria Corsello,2 Anna Vittoria Ciardullo,1 Alfredo Pontecorvi,2 and Laszlo Hegedu¨s ( 2014) 'The Impact of Graves’ Disease and Its Treatment on Handwriting Characteristics', Mary Ann
Liebert, Inc., 24,[Online].(Accessed: Number 8, 2014).
05/01/2023 30
Graves’ Disease(cont.)
(A) During hyperthyroidism فرط
الدرقية الغدة ,نشاطhandwriting is hypertrophic and contracted with several angles.
(B) Post treatment, in the euthyroid State الحالة ف the handwriting is , العاديةcharacterized by an increased fluidity.
Standard text written by Seventy-year-old female with Graves’ disease
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Graves’ Disease(cont.)
In the euthyroid state (B) the size of the letters (dotted line) increases compared to the hyperthyroid state (A).
whereas extensions of letters (white and gray arrows) and angles (black arrows) are reduced
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Graves’ Disease(cont.)
Thirty-six-year-old female with Graves’ disease. Following recovery from hyperthyroidism
the distance between the words (black dotted line)
and the distance between the letters (gray line) are reduced,
whereas the width of the letters (arrow) increased.
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Arabic handwriting
WriterIdentificati
on
Prediction of Age, Gender,
and Nationalit
y
Handedness
Detection
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Arabic Handwriting analysisDatabase Features Classifiers AccuracyPrinted text 20 different characters fonts(320 text images printed)Handwritten text 22 persons (132 handwriting )
Texture features using (16 Gabor filters)
WED(weighted Euclidian Distance)
10 writers , 20 Arabic images
multi-scale edge-hinge features
grapheme features
K-NN
AHDB Dataset100 writer (32,000 Arabic word)
Edge-direction distributionMoment invariantsWord measurements (Area , Height,length from baseline to upper edge,length from baseline to the lower edge )
K-NN
QUWI database that contains both Arabic and English handwritings($commercially)1017 WRITERS
Directionsاتجاه
Curvaturesتقوس
Tortuosity تعرج
chain codes
edge-based directional
K-NN
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Arabic Handwriting analysis
Database Features Classifiers AccuracyQUWI (Arabic and English handwritings($commercially)
Directionsاتجاه
curvaturesتقوس
Tortuosityتعرج
chain codes
edge-based directional
Random forest
,Kernel discriminant analysis using spectral regression
120 Farsi handwriting samples
Left and right margins
Word expansion
Letter size
Line and word spacing
Line skew
The ratio of vertical to horizontal elongation of words
Slant
SVM
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Summary# English ArabicCommon Features
size of lettersslant of letters and wordsbaselinepen pressurespacing between letters and wordsBreaks(connected disconnected)MarginsSpeed
Edge-direction distributionMoment invariantsWord measurementsDirectionsاتجاهcurvaturesتقوسTortuosityتعرجchain codes
Classifiers
SVM(7) K-NN(3),Random forest
Database
IAM,BFL AHDB(100 WRITERS) ,QUWI
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AgendaIntroductionApplicationsHandwriting analysis on-line vs. Off-line.Features for Arabic vs. English writingsResearch PlanReferences
05/01/2023 38
Research Plan
Building Android Application For Online Arabic Graphology .
We will work on available Database Arabic and English for writer identification with an improved set of features and classification methods.
After that we will work on forgery signatures with real Arabic dataset.
We aspires to work on Diseases diagnoses in Early Stages with Arabic dataset ,It will required building a database of real patients .
Goal
First
Second
Future work
05/01/2023
References(English)1. Champa H N,Dr. K R AnandaKumar (2010) 'Artificial Neural Network for
Human Behavior Prediction through Handwriting Analysis', International Journal of Computer Applications(0975 – 8887), 2(2), pp. 36-41 ,(Accessed: May 2010).
2. Shitala Prasad,Vivek Kumar Singh,Akshay Sapre (2010) Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine, International Journal of Computer Applications (0975 – 8887), pp. 25-29 ,8(12), (Accessed: October 2010).
3. Vikram Kamath, Nikhil Ramaswamy, P. Navin Karanth, Vijay Desai and S. M. Kulkarni (2011) 'DEVELOPMENT OF AN AUTOMATED HANDWRITING ANALYSIS SYSTEM', ARPN Journal of Engineering and Applied Sciences , 6(9), pp. 135-140 [Online]. Available at: www.arpnjournals.com (Accessed: SEPTEMBER 2011).
4. UZANNA GÓRSKA,ARTUR JANICKI (2012) 'RECOGNITION OF EXTRAVERSION LEVEL BASED ON HANDWRITING AND SUPPORT VECTOR MACHINES1',Perceptual and Motor Skills 114, 3, 857-869, pp. 858-869 [Online]. Available at:(Accessed: May 31, 2012.).
5. Rashi Kacker and Hima Bindu Maringanti, (2012) 'Personality Analysis Through Handwriting', GSTF Journal on Computing (JoC), 2(1), pp. 858-869 [Online]. (Accessed: April 2012).
6. Abdul Rahiman M,Diana Varghese,Manoj Kumar G (2013) 'HABIT: Handwritten Analysis Based Individualistic Traits Prediction', International Journal of Image Processing (IJIP), 7(2), pp. 209-218 [Online]. Available at: (Accessed: 2013).
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6-Abdul Rahiman M,Diana Varghese,Manoj Kumar G (2013) 'HABIT: Handwritten Analysis Based Individualistic Traits Prediction', International Journal of Image Processing (IJIP), 7(2), pp. 209-218 [Online]. Available at: (Accessed: 2013).7-Esmeralda C Djamal, Sheldy Nur Ramdlan, Jeri Saputra (2013) 'Recognition of Handwriting Based on Signature and Digit of Character Using Multiple of Artificial Neural Networks in Personality Identification , Information Systems International Conference (ISICO), 2(4), pp. 411-415 [Online]. (Accessed: December 2013).8-Sandeep Dang,Prof. Mahesh Kumar, Mahesh (2014) 'Handwriting Analysis of Human Behaviour Based on Neural Network', International Journal of Advanced Research in Computer Science and Software Engineering, 4(9), pp. 227-232 [Online]. Available at:www.ijarcsse.com (Accessed: September 2014).9-Luiz S. OLIVEIRA a , Edson JUSTINO a , Cinthia FREITAS a and Robert SABOURINb (2005) 'The Graphology Applied to Signature Verification', ,(Retrieved on:10 December2015).10-Abdelâali Hassaïne,Somaya Al-ma'adeed (2012) 'An Online Signature Verification System for Forgery and Disguise Detection', [Online]. : (Accessed: NOVEMBER 2012). Retrieved on: 07 October 201511-Omar Santana, Carlos M. Travieso, Jesus B. Alonso, Miguel A. Ferrer (2010) 'Writer Identification Based on Graphology Techniques', IEEE A&E SYSTEMS MAGAZINE,,(), pp. [Online]. Available at: (Accessed: JUNE 2010).12-R. K. Hanusiak · L. S. Oliveira · E. Justino · R. Sabourin (2011) 'Writer verification using texture-based features', Springer, (), pp. 214 -226,[Online]. (Accessed: 24 May 2011).
References(English)
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13-D. Bertolini a, L.S. Oliveira a,⇑, E. Justino b, R. Sabourin c ( 2012) 'Texture-based descriptors for writer identification and verification ', Elsevier Ltd, 40(6), pp. 2069–2080 [Online]. Available at: 18 October 2012 (Accessed: May 2013).14-A. M. M. M. Amaral, C. O. A. Freitas, F. Bortolozzi. “The Graphometry applied to writer identification”. In Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, USA, vol.1, pp.10-16, 2012.15-Aline Maria M. M. Amaral1,2, Cinthia O. A. Freitas2, and Flavio Bortolozzi1. “2013)Multiple Graphometric Features for Writer Identification as part of Forensic Handwriting Analysis”. In Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, USA, vol.1, pp.10-16, 2013.16-A. Hassa¨ıne, S. Al-Maadeed, and A. Bouridane, “A set of geometrical features for writer identification,” Neural Information Process. Berlin Heidelberg: Springer,, vol. 45, pp. 584–591,2012.17-E Khalifa\ S Al-Maadeed2, M A Tahir3, F Khelifil and A Bouridane1 ( 2013) 'OFF-LINE WRI TER I DENTIF ICATI ON U S ING MULTI- SCALE LOCAL BINARY PATTERNS AND SR-KDA', IEEE, [Online]. 18-Shweta Hegade1, Gargee Hiray2, Prajkta Mali3, Prof. Punam Raskar4 (2015) 'FODEX: Forensic Document Examiner –Using Graphology Science', IJETST, 2(3), pp. 2042-2045 [Online]. Available at: (Accessed: March 2015).19-[2008] Joel Engel , Early Cancer Detection through Graphology Analysis.20-Giampaolo Papi,1,2 Cristina Botti,3 Salvatore Maria Corsello,2 Anna Vittoria Ciardullo,1 Alfredo Pontecorvi,2 and Laszlo Hegedu¨s ( 2014) 'The Impact of Graves’ Disease and Its Treatment on Handwriting Characteristics', Mary Ann Liebert, Inc., 24,[Online].(Accessed: Number 8, 2014).
References(English)
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21-FEDDAOUI Nadia, HAMROUNI Kamel (2006) 'Personal identifi'cation based on texture analysis of Arabic handwriting text', IEEE, (), pp. 1302-1307 [Online]. 22-Somaya Al-Ma’adeed, Amat-AlAleem Al-Kurbi, Amal Al-Muslih, Reem Al-Qahtani, Haend Al Kubisi (2008) 'Writer Identification of Arabic Handwriting Documents Using Grapheme Features', IEEE, (), pp. 923-924 [Online].23-Somaya Al-Ma’adeed, Eman Mohammed, Dori Al Kassis, Fatma Al-Muslih, (2008) 'Writer Identification using Edge-based Directional Probability Distribution Features for Arabic Words', IEEE, (), pp. 582-590 [Online]. 24-Somaya Al-Maadeed (2012) 'Text-DependentWriter Identification for Arabic Handwriting', Journal of Electrical and Computer Engineering, 2012(), pp. 8 [Online].25-Somaya Al Maadeed, Wael Ayouby, Abdelˆaali Hassa¨ıne, Jihad Mohamad Aljaam (2012) 'QUWI: An Arabic and English Handwriting Dataset for Offline Writer Identification', IEEE, (), pp. 746-751 [Online]. 26-Somaya Al–Maadeed, Fethi Ferjani, Samir Elloumi, Abdelaali Hassaine and Ali Jaoua (2013) 'Automatic Handedness Detection from Off-Line Handwriting', IEEE, (), pp. 119-124 [Online]. 27-Al Maadeed and Hassaine: Automatic prediction of age, gender, and nationality in offline handwriting. EURASIP Journal on Image and Video Processing 2014 2014:10.28-Somayeh Hashemi1, Behrouz Vaseghi2, Fatemeh Torgheh3 (2015) 'Graphology for Farsi Handwriting Using Image Processing Techniques', IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), 10(3), pp. 01-07 [Online]. Available at:(Accessed: May - Jun.2015).
References(Arabic)
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Thank you