Graphology research

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GRAPHOLOGY RESEARCHES

Prepared byButhainah hamdy

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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

behavior prediction

Forensic

Disease diagnosis

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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.)• 1-[2010] International Journal of Computer Applications, Artificial Neural Network for Human

Behavior Prediction through Handwriting Analysis.

• 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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•

Baseline

Ascending

Descendinglevel

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Pen pressur

e

Light writer Medium writer

Heavy/deep writer

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Behavior Prediction through Handwriting Analysis.• 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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Human behavior prediction(cont.)• 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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Human behavior prediction(cont.)• 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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Segmentation Method for Prediction of Human Personality using Support Vector Machine.• Image Handwriting Pre-Processing

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Segmentation Method for Prediction of Human Personality using Support Vector Machine.• Image handwriting segmentation and feature extraction

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Method for Prediction of Human Personality using Support Vector Machine.• Feature extraction

• 1-size of the letters

• 2-Slant of Words and Letters• To calculate the slant in world and letter we use simpletrigonometric 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 متعاطف to 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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Segmentation Method for Prediction of Human Personality using Support Vector Machine.• Feature extraction

• 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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Segmentation Method for Prediction of Human Personality using Support Vector Machine.• 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 dimensionfeature space using RBF kernel function.3. Then a division global hyperplane is computed toseparate the feature space optimally to the classes of the training vector samples.

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Human behavior prediction(cont.)• 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 cancer, زهايمرor Alzheimer's disease شللthrough the variation in features obtained over a period of time -2 forensic document examination-3lie 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 lettersPressure of the writingNumber of breaksMargins

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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• FEATURE DETECTION BY AHWAS • 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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• FEATURE DETECTION BY AHWAS • 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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• FEATURE DETECTION BY AHWAS • 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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• FEATURE DETECTION BY AHWAS • 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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Human behavior prediction(cont.)• 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 fromfeatures 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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Human behavior prediction(cont.)• 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 fatigueمجهده .

• 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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Human behavior prediction(cont.)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

behavior prediction

Forensic

Disease diagnosis

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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.) • 1-[2013] Expert Systems with Applications ,Texture-based descriptors for writer

identification and verification.• Databases

Datasets

IAM databaseBrazilian forensic letters database(BFL)

315 writers, with three samplesper 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 leastfour pages.

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Forensice (CONT.) • 1-[2013] Expert Systems with Applications ,Texture-based descriptors for writer

identification and verification.• 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 tediousممل and can provide doubts about the writeridentification.

• 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.) • 2-The Graphometry Applied to Writer Identification

• 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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• Proposed Method• Consist of two stage

• Training stage• Test stage

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• 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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Automatic, prediction of age, gender, and nationality in offline handwriting.• 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 representationand 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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Automatic, prediction of age, gender, and nationality in offline handwriting.• features:

• Directionsاتجاه • curvaturesتقوس • Tortuosityتعرج • chain codes• edge-based directional

• Classifiers• random forests classifier • Kernel discriminant analysis using spectral regression

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Automatic, prediction of age, gender, and nationality in offline handwriting.• 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

Handwriting by eight different writers.

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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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Dependent Writer Identification for Arabic Handwriting.

• 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 bySrihari 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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Dependent Writer Identification for Arabic Handwriting.• System Overview

Scanned handwriting

preprocessing

Feature extraction

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.

Training and testing phases in the text-dependent writer identification system.

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Dependent Writer Identification for Arabic Handwriting.• 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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Dependent Writer Identification for Arabic Handwriting.• Preprocessing

Page Scanning

Document Segmentation

Removing Background

Edge Detection

segments the page into words and stores eachword in a separate file.

used Otsu’s method

Sobel edge detection

scanned at a resolution of 200 by 200 pixels.

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Dependent Writer Identification for Arabic Handwriting.• Feature Extraction

1-The edge-direction distribution

2-Moment Invariants

3-word measurements feature extraction

4-angle

8-angle

16-angle

12-angle

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Dependent Writer Identification for Arabic Handwriting.• Feature Extraction

• 1-Area.• 2-length.• 3-Height.• 4-Height from the Baseline to the Upper Edge.• 5-Baseline to the Lower Edge.

3-word measurements feature extraction

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Applications

behavior prediction

Forensic

Disease diagnosis

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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• [2009] Early Cancer Detection through Graphology Analysis• 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.

Age 28

Age 33

Age 41

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Medical diagnosis• [2009] Early Cancer Detection through Graphology Analysis• Finding Cancer in Its Early Stages• 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 unstable مستقر غير

• Clear interruptions between descending and ascending strokes are also visible.

Age 28

Age 33

Age 41

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Medical diagnosis• [2009] Early Cancer Detection through Graphology Analysis• Finding Cancer in Its Early Stages

• 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.

Age 28

Age 33

Age 41

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REFERENCES1.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/

http://handwriting.qu.edu.qa/dataset/

http://handwriting.qu.edu.qa/dataset/

http://tc11.cvc.uab.es/datasets/type/

http://tc11.cvc.uab.es/datasets/type/

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