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Graphology .

The document discusses automated Arabic graphology, focusing on how handwriting analysis reveals insights into personality through both conscious and subconscious writing behaviors. It covers various applications of graphology, including personality prediction and disease diagnosis, while comparing handwriting analysis methods online and offline. Additionally, it outlines a research plan to develop an Android application for online Arabic graphology and to create a database for handwriting analysis.

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Automated Arabic Graphology 3/19/2016 1 Faculty of Computers and Information , Menoufiya University Presented by Buthainah Hamdy
Agenda Introduction Applications Handwriting analysis on-line vs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 2
3/19/2016 3 Introduction Brain writing
3/19/2016 4 Introduction(cont.) Brain writing Conscious Mind Control-WHAT we write SUB-Conscious Mind Controls-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.
 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. 3/19/2016 5 Introduction(cont.) Brain writing
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. 3/19/2016 6 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.
 Graphology reveals insights into the mental, physical of the writer. 3/19/2016 7 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
Common Features of Graphology 3/19/2016 8 Introduction(cont.) Size Baseline Pressure
Introduction(cont.) 3/19/2016 9 Slant Zones Speed in writing And Margins Spacing between letters ,words and line Common Features of Graphology
Introduction(cont.) 3/19/201610 personality Arabic English ‫/التهكم‬ Sarcasm ‫الذات‬ ‫تقدير‬ ‫/عدم‬ Low self- esteem ‫للذات‬ ‫عالي‬ ‫/احترام‬ High self - esteem Personality analysis in Arabic and English
3/19/2016 11 personality Arabic English ‫/اصرار‬ Persistence ‫/عدوانية‬ Aggressive ‫فكر‬ ‫/سيولة‬ Fluidity of thoughts Introduction(cont.) Personality analysis in Arabic and English
3/19/2016 12 personality Arabic English ‫مسحوب‬‫عاطفيا‬ / Emotionally withdrawn ‫الشخصية‬ ‫/مزدوج‬ Dual personality ‫/دبلوماسي‬ diplomacy Introduction(cont.) Personality analysis in Arabic and English
3/19/2016 13 personality Arabic English ‫/مجادل‬ argumentative ‫وسيطرة‬ ‫/هيمنة‬ dominant ‫عالي‬ ‫/تركيز‬ concentration Introduction(cont.) Personality analysis in Arabic and English
3/19/2016 14 personality Arabic English ‫/غامض‬ secretive ‫/الكذب‬ laying ‫المشاعر‬ ‫/اتزان‬ ambivert Introduction(cont.) Personality analysis in Arabic and English
Agenda Introduction Applications Handwriting analysis on-line vs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 15
Applications 3/19/2016 16 Graphology Personality prediction Forensic Diseases diagnosis 1-Human behavior (Extraversion) 2-Marital compatibility 3-Business compatibility 4-RECRUITMENT (Employment profiling) 5-Education 6-Lie detector 1-Writer identification 2-Investigations 3-Age,gender , nationality and handedness 4-Forged Signatures 1-Mental diseases Suicide, Alzheimer, Schizophrenia and Depression analysis 2-physical diseases Heart, cancer , Hypothyroidism (graves’ diseases) and Parkinson's Disease
Agenda Introduction Applications Handwriting analysis on-line vs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 17
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 3/19/2016 18
Agenda Introduction Applications Handwriting analysis on-line vs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 19
3/19/2016 20 Personality Prediction Human behavior Extraversio n detection Employment profiling
Human behavior Database Features Classifiers Accuracy Multiple samples Baseline pen pressure Height 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 of Age between (20-24) 100 words size of letters slant of letters and words baseline pen pressure spacing between letters and words Breaks(connected&disconnecte d) Margins Speed AHWAS (Automated Handwriting Analysis System) calibrated with manual analysis. 883 writers (404men ,479 women) age from 20 to 30 years Size Width of middle zone letters Slant Size of margins The way of ending the verse Angularity Stability of pressure SVM(Support Vector machine) 3/19/2016 21
Human behavior 3/19/2016 22 Database Features Classifiers Accuracy 50 samples Margins - Baseline Size - Zonal ratio Slant - Space Degree of connection Myer Briggs dichotomies Based on Keirsey’s temperament sorter. handwriting samples Slant - size Pressure - word spacing line spacing - Baseline Least Squares Linear Regression 100 data set for signature and 156 type of 26 characters Curved start - End Streak Shell - middle streaks Underline - Extreme margin Dot structure - Separate Streaks disconnected Learning Vector Quantization (LVQ) for letters, ANN and multi-structure for signature 10 signatures Curved start - End Streak Shell - middle streaks Underline - Extreme margin Dot structure - Separate Streaks disconnected ANN and multi-structure
Forensic Writer Identification Age, Nationality, Gender and Handedness recognition 3/19/2016 23
Forensic 3/19/2016 24 Database Features Classifiers Accuracy 5,600 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 Distances Angles Speed Angular speeds Using multiple classifiers 1-Random Forest 2-logistic regression 3-linear regression 4-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, Pressure VowelinfoA, 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 , Progression Proportion , Pressure Entry/Exit points , Slant GLCM descriptors SVM(support Vector machine) dissimilarity representation
Forensic 3/19/2016 25 Database Features Classifiers Accuracy (BFL) 315 writers , IAM database 650 writers texture descriptor local 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 lines Proportion of black pixels Right margin position. The lower left margin position. Upper margin position Bottom margin position Height of the first word Axial slant SVM(support vector machine) lAM English handwriting dataset(657 different writers ) Directions Curvatures Tortuosity Chain code Edge 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)
Diseases diagnosis 3/19/2016 26
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” 3/19/2016 27 [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. 3/19/2016 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
Graves’ Disease(Manual analysis) Objectives:  Evaluate handwriting characteristics before and after therapy for hyperthyroid Graves’ disease (GD).(En)[20] 3/19/2016 29 Database Features Classifier 22patients (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).
Graves’ Disease(cont.) 3/19/2016 30 (A) During hyperthyroidism ‫الغدة‬ ‫نشاط‬ ‫فرط‬ ‫,الدرقية‬ handwriting is hypertrophic and contracted with several angles. (B) Post treatment, in the euthyroid State ‫العادية‬ ‫الحالة‬ ‫,ف‬ the handwriting is characterized by an increased fluidity. Standard text written by Seventy-year-old female with Graves’ disease
Graves’ Disease(cont.) 3/19/2016 31 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
Graves’ Disease(cont.) 3/19/2016 32 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.
Arabic handwriting 3/19/2016 33
Arabic Handwriting analysis 3/19/2016 34 Database Features Classifiers Accuracy Printed 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 Dataset 100 writer (32,000 Arabic word) Edge-direction distribution Moment invariants Word 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 ($commercially) 1017 WRITERS Directions‫اتجاه‬ Curvatures‫تقوس‬ Tortuosity ‫تعرج‬ chain codes edge-based directional K-NN
Arabic Handwriting analysis 3/19/2016 35 Database Features Classifiers Accuracy QUWI (Arabic and English handwritings ($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
Summary # English Arabic Common Features size of letters slant of letters and words baseline pen pressure spacing between letters and words Breaks(connected disconnected) Margins Speed Edge-direction distribution Moment invariants Word measurements Directions‫اتجاه‬ curvatures‫تقوس‬ Tortuosity‫تعرج‬ chain codes Classifier s SVM(7) K-NN(3),Random forest Database IAM,BFL AHDB(100 WRITERS)3/19/2016 36
Agenda Introduction Applications Handwriting analysis on-line vs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 37
Research Plan 3/19/2016 38 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
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). 3/19/2016 39
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 2015 11-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). 3/19/2016 40 References(English)
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,3/19/2016 41 References(English)
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).3/19/2016 42 References(Arabic)
Thank you 3/19/2016 43

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Graphology .

  • 1.
    Automated Arabic Graphology 3/19/2016 1 Facultyof Computers and Information , Menoufiya University Presented by Buthainah Hamdy
  • 2.
    Agenda Introduction Applications Handwriting analysis on-linevs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 2
  • 3.
  • 4.
    3/19/2016 4 Introduction(cont.) Brain writing ConsciousMind Control-WHAT we write SUB-Conscious Mind Controls-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.
  • 5.
     Handwriting occursthrough 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. 3/19/2016 5 Introduction(cont.) Brain writing
  • 6.
    Graphology is aword originated from Greek language. The first person that carried out systematic observations on the manner of handwriting was Camillo Baldi in 1622 AD. 3/19/2016 6 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.
  • 7.
     Graphology revealsinsights into the mental, physical of the writer. 3/19/2016 7 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
  • 8.
    Common Features ofGraphology 3/19/2016 8 Introduction(cont.) Size Baseline Pressure
  • 9.
    Introduction(cont.) 3/19/2016 9 Slant Zones Speedin writing And Margins Spacing between letters ,words and line Common Features of Graphology
  • 10.
    Introduction(cont.) 3/19/201610 personality Arabic English ‫/التهكم‬ Sarcasm ‫الذات‬‫تقدير‬ ‫/عدم‬ Low self- esteem ‫للذات‬ ‫عالي‬ ‫/احترام‬ High self - esteem Personality analysis in Arabic and English
  • 11.
    3/19/2016 11 personality ArabicEnglish ‫/اصرار‬ Persistence ‫/عدوانية‬ Aggressive ‫فكر‬ ‫/سيولة‬ Fluidity of thoughts Introduction(cont.) Personality analysis in Arabic and English
  • 12.
    3/19/2016 12 personality ArabicEnglish ‫مسحوب‬‫عاطفيا‬ / Emotionally withdrawn ‫الشخصية‬ ‫/مزدوج‬ Dual personality ‫/دبلوماسي‬ diplomacy Introduction(cont.) Personality analysis in Arabic and English
  • 13.
    3/19/2016 13 personality ArabicEnglish ‫/مجادل‬ argumentative ‫وسيطرة‬ ‫/هيمنة‬ dominant ‫عالي‬ ‫/تركيز‬ concentration Introduction(cont.) Personality analysis in Arabic and English
  • 14.
    3/19/2016 14 personality ArabicEnglish ‫/غامض‬ secretive ‫/الكذب‬ laying ‫المشاعر‬ ‫/اتزان‬ ambivert Introduction(cont.) Personality analysis in Arabic and English
  • 15.
    Agenda Introduction Applications Handwriting analysis on-linevs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 15
  • 16.
    Applications 3/19/2016 16 Graphology Personality prediction Forensic Diseases diagnosis 1-Human behavior (Extraversion) 2-Maritalcompatibility 3-Business compatibility 4-RECRUITMENT (Employment profiling) 5-Education 6-Lie detector 1-Writer identification 2-Investigations 3-Age,gender , nationality and handedness 4-Forged Signatures 1-Mental diseases Suicide, Alzheimer, Schizophrenia and Depression analysis 2-physical diseases Heart, cancer , Hypothyroidism (graves’ diseases) and Parkinson's Disease
  • 17.
    Agenda Introduction Applications Handwriting analysis on-linevs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 17
  • 18.
    Handwriting analysis on-linevs. 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 3/19/2016 18
  • 19.
    Agenda Introduction Applications Handwriting analysis on-linevs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 19
  • 20.
  • 21.
    Human behavior Database FeaturesClassifiers Accuracy Multiple samples Baseline pen pressure Height 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 of Age between (20-24) 100 words size of letters slant of letters and words baseline pen pressure spacing between letters and words Breaks(connected&disconnecte d) Margins Speed AHWAS (Automated Handwriting Analysis System) calibrated with manual analysis. 883 writers (404men ,479 women) age from 20 to 30 years Size Width of middle zone letters Slant Size of margins The way of ending the verse Angularity Stability of pressure SVM(Support Vector machine) 3/19/2016 21
  • 22.
    Human behavior 3/19/2016 22 DatabaseFeatures Classifiers Accuracy 50 samples Margins - Baseline Size - Zonal ratio Slant - Space Degree of connection Myer Briggs dichotomies Based on Keirsey’s temperament sorter. handwriting samples Slant - size Pressure - word spacing line spacing - Baseline Least Squares Linear Regression 100 data set for signature and 156 type of 26 characters Curved start - End Streak Shell - middle streaks Underline - Extreme margin Dot structure - Separate Streaks disconnected Learning Vector Quantization (LVQ) for letters, ANN and multi-structure for signature 10 signatures Curved start - End Streak Shell - middle streaks Underline - Extreme margin Dot structure - Separate Streaks disconnected ANN and multi-structure
  • 23.
  • 24.
    Forensic 3/19/2016 24 Database FeaturesClassifiers Accuracy 5,600 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 Distances Angles Speed Angular speeds Using multiple classifiers 1-Random Forest 2-logistic regression 3-linear regression 4-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, Pressure VowelinfoA, 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 , Progression Proportion , Pressure Entry/Exit points , Slant GLCM descriptors SVM(support Vector machine) dissimilarity representation
  • 25.
    Forensic 3/19/2016 25 Database FeaturesClassifiers Accuracy (BFL) 315 writers , IAM database 650 writers texture descriptor local 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 lines Proportion of black pixels Right margin position. The lower left margin position. Upper margin position Bottom margin position Height of the first word Axial slant SVM(support vector machine) lAM English handwriting dataset(657 different writers ) Directions Curvatures Tortuosity Chain code Edge 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)
  • 26.
  • 27.
    Graphologists have determinedthat 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” 3/19/2016 27 [2008] Joel Engel , Early Cancer Detection through Graphology Analysis. Variations of normal handwriting Down Strokes Up Strokes
  • 28.
    Earlier detecting cancer(cont.) Finding Cancer in Its Early Stages  Samples of microphotographs of Mrs. B’s handwriting. 3/19/2016 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
  • 29.
    Graves’ Disease(Manual analysis) Objectives: Evaluate handwriting characteristics before and after therapy for hyperthyroid Graves’ disease (GD).(En)[20] 3/19/2016 29 Database Features Classifier 22patients (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).
  • 30.
    Graves’ Disease(cont.) 3/19/2016 30 (A)During hyperthyroidism ‫الغدة‬ ‫نشاط‬ ‫فرط‬ ‫,الدرقية‬ handwriting is hypertrophic and contracted with several angles. (B) Post treatment, in the euthyroid State ‫العادية‬ ‫الحالة‬ ‫,ف‬ the handwriting is characterized by an increased fluidity. Standard text written by Seventy-year-old female with Graves’ disease
  • 31.
    Graves’ Disease(cont.) 3/19/2016 31 Inthe 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
  • 32.
    Graves’ Disease(cont.) 3/19/2016 32 Thirty-six-year-oldfemale 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.
  • 33.
  • 34.
    Arabic Handwriting analysis 3/19/201634 Database Features Classifiers Accuracy Printed 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 Dataset 100 writer (32,000 Arabic word) Edge-direction distribution Moment invariants Word 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 ($commercially) 1017 WRITERS Directions‫اتجاه‬ Curvatures‫تقوس‬ Tortuosity ‫تعرج‬ chain codes edge-based directional K-NN
  • 35.
    Arabic Handwriting analysis 3/19/201635 Database Features Classifiers Accuracy QUWI (Arabic and English handwritings ($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
  • 36.
    Summary # English Arabic Common Features sizeof letters slant of letters and words baseline pen pressure spacing between letters and words Breaks(connected disconnected) Margins Speed Edge-direction distribution Moment invariants Word measurements Directions‫اتجاه‬ curvatures‫تقوس‬ Tortuosity‫تعرج‬ chain codes Classifier s SVM(7) K-NN(3),Random forest Database IAM,BFL AHDB(100 WRITERS)3/19/2016 36
  • 37.
    Agenda Introduction Applications Handwriting analysis on-linevs. Off-line. Features for Arabic vs. English writings Research Plan References 3/19/2016 37
  • 38.
    Research Plan 3/19/2016 38 BuildingAndroid 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
  • 39.
    References(English) 1. Champa HN,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). 3/19/2016 39
  • 40.
    6-Abdul Rahiman M,DianaVarghese,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 2015 11-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). 3/19/2016 40 References(English)
  • 41.
    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,3/19/2016 41 References(English)
  • 42.
    21-FEDDAOUI Nadia, HAMROUNIKamel (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).3/19/2016 42 References(Arabic)
  • 43.

Editor's Notes

  • #8 When you write, your pen is under the control of the muscles of your fingers, hands and arm, All these body parts are under the control of your mind.
  • #19 Online modality (through WACOM Intous4 digitizing tablet ) Offline modality (scanned copies of signatures)
  • #29 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. 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 unstable غير مستقر Clear interruptions between descending and ascending strokes are also visible. 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
  • #34 The study of Arabic handwriting identification is limited The recognition of Arabic characters is also important for certain non-Arabic-speaking languages, such as Farsi, Kurd, Persian, and Urdu.