Graphology .

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Automated Arabic Graphology

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Faculty of Computers and Information , Menoufiya University

Presented by

Buthainah Hamdy

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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


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personality Arabic English/مجادلargumentative

وسيطرة /هيمنةdominant

عالي /تركيزconcentration


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personality Arabic English/غامضsecretive

/ الكذبlaying

المشاعر /اتزانambivert


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Agenda


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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

http://www.doctoroz.com/videos/alzheimers-5-greatest-risk-factors

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Agenda


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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


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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)


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


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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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).

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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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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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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

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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

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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

Graphology .

Technology

Transcript of Graphology .