The document describes a project to develop a system called FODEX (Forensics Document Examiner) that examines documents and generates an algorithmic analysis of human handwriting. It will work by comparing different handwriting samples using image processing algorithms. The system will preprocess images, perform thinning, segmentation, feature extraction and profile generation to analyze handwriting traits defined in graphology and generate a personalized profile or report for the user. It aims to provide an automated, user-friendly and accurate alternative to existing handwriting analysis systems. The project plan outlines the implementation progress and technical details like using Java and libraries for image processing, MySQL database, and UML diagrams.

1. Forensics Document Examiner – using
graphology science(FODEX)
Group Members
SHWETA HEGADE (B80364283)
GARGEE HIRAY (B80364285)
PRAJKTA MALI (B80364335)
AISHWARYA SHINDE (B80364400)
fde.grapho@gmail.com
STES’S SMT. KASHIBAINAVALECOLLEGE
OF ENGINEERING
SAVITRIBAI PHULE PUNE UNIVERSITY
Under the guidance of
Prof.Punam Raskar

2. Overview
• Problem Statement
• About Graphology
• System Working
• Feasibility assessment
• Basic Workflow Diagram
• Algorithms
• UML Diagrams
• Mathematical Model
• FODEX and Other systems
• Implementation till date
• Project Plan
• Bibliography
SKNCOE, Department of
Computer Engineering 2014-15

3. SPONSORED BY
Centre for Personality Assessment and Graphology
• They provide Training and Services in Handwriting,
Signature, Colour, Logo Analysis for recruitment,
employee assessment, personality assessment,
counseling, business consultation, forensic
document examination.
• http://www.cpag.in/
• https://www.facebook.com/CPAGP
SKNCOE, Department of
Computer Engineering 2014-15

4. Problem Statement
• Developing a system which will examine
document and generate a algorithmic
analysis of human handwriting by
comparison of different samples of
handwriting, through image processing
algorithms .
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5. About Graphology
•History
•Meaning of Graphology
•How graphology works?
•What can be revealed?
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6. What do handwriting analyst checks in handwriting ?
- LOOPS,MARGINS,BASELINE,SIZE,SPACING ,PRESSURE,SLANTS,
ZONES, CONNECTIONS, PASTOCITY,TRAIT METHOD
Example:-
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8. •Forensics and graphology
•How our system works?
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9. Feasibility assessment
P- Complete
NP- Complete
NP- Hard
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10. Workflow
• Image preprocessing
• Thinning
• Segmentation
• Feature extraction
• Profile generation
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11. Work Flow
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Engineering 2014-15
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12. Basics of Image Processing
•Image
•Pixel
•Types of Images
–Vector
–Digital
•Binary
•Grayscale
•Color
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13. RGB splitting
•Color images are often built of several stacked color
channels, each of them representing value levels of the
given channel. For example, RGB images are composed of
three independent channels for red, green and blue primary
color components.
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14. Grayscale
•Grayscale images are distinct from one-bit bi-tonal black-
and-white images, which in the context of computer
imaging are images with only the two colors, black,
and white (also called bi-level or binary images). The value
of each pixel is a single sample.
•Grayscale images have 256of gray .
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15. continue
•How all grayscale algorithms fundamentally work
•All grayscale algorithms utilize the same basic three-step
process:
1. Get the red, green, and blue values of a pixel
2. Convert these pixel values into gray value.
3. Replace the original red, green, and blue values with the
new gray value
•Basic Formula:
Gray = (Red + Green + Blue) / 3
( (0.3 * R) + (0.59 * G) + (0.11 * B) )
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16. SKNCOE, Department of
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Blurring/Softening
Gaussian Blur salt and pepper
Noise

17. Threshold Detection
•Thresholding is a non-linear operation that converts a gray-
scale image into a binary image where the two levels are
assigned to pixels that are below or above the specified
threshold value.
•Thresholding Methods:
–Histogram shape-based methods.
•E.g. :Ostu’s Method ,Kapur’s Method.
–Clustering-based methods
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18. SKNCOE, Department of
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Thresholding of An image
Fig.[A ] Fig.[b]

19. Thinning
•Thinning algorithm is a Morphological operation that is
used to remove selected foreground pixels from binary
images.
•Thinning Methods:
–Iterative Thinning
•e.g.: Stentiford Thinning Algorithm, Zhang-Suen
Thinning Algorithm,etc.
–Non-Iterative Thinning
•e.g.: Neusius-Olszewski Algorithm
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20. SKNCOE, Department of
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Segmentation
•Separation or Isolation of Image of Handwritten Document
Text into
–Lines
–Words
–Characters
•Methods of Segmentation
–Histogram based
–Profile and contour based
–Background analysis

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•Segmentation of Image of Handwritten Text.

22. •Segmentation of Image of Handwritten Text.

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•Segmentation of Image of Handwritten Text.

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Histogram based segmentation of Image of
Handwritten text
An "image histogram" is a type of histogram that acts as
a graphical representation of the tonal distribution in
a digital image. It plots the number of pixels for each tonal
value. Image histogram shows frequency of pixels intensity
values.

25. Image Scaling
• Image scaling is the process of resizing a digital image.
•Methods of scaling
–Nearest neighbor interpolation
–Bilinear Interpolation
–Supersampling
–Vectorization
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26. SKNCOE, Department of
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FODEX and other systems
Other systems
•Handwriting University’s Handwriting Wizard.
Web-based computerized handwriting analysis system. The system
consists of 9 handwriting characteristics. Each characteristic consists of
one or more questions. Platform independent. It is a free handwriting
analysis system. Text-based report type.
•Sheila Lowe’s Handwriting Analyzer.
Applying Gestalt or holistic graphology concept. Handwriting
characteristics such as line spacing, margin, slant and pressure are
represented by categories. At least 10 categories must be completed to
generate a report. A standalone system. Runs on Windows.
3/14/2015

27. •Advantage of FODEX over other systems
–User Friendly
–Faster
–Automated
–No other input from User beyond their Handwriting
–More Accurate
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28. SKNCOE, Department of
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Technical Details
•JDK 1.7
Open Source Libraries for Image processing
ImageJ, Fiji, Commons Imaging, Endrov, LeadTools ,
OpenCv, etc
•Java Web Start
•MySQL
3/14/2015

29. UML DIAGRAM
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31. Mathematical Model.
Let S (be a main set of) ≡ {I, Isp, Igs, Ib, Itd, Ibt, Ith, Isg, Isc,
If, Iout, DS}
•Where,
•S is the system.
•I is the image
•DS is Data Set
•Isp is RGB splitting of image
•Isp=RGBSplitting(I)
•[sepB= RGBcolor AND 0xff
•sepG= (RGBcolor >>8) AND 0xff
•sepR=(RGBcolor>>16) AND 0xff]
•
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32. Continue..
Igs=Grayscale(Isp)
[Igs=(sepR+sepG+sepB)/3]
Ib=GaussianBlur(Igs)
Itd=Threshold(Ib)
[Threshold th=128
If(gs>th) then gs=1 else gs=0]
Ibt=Binary(Itd)
Ith=Thinning(Ibt)
Isg=Segmnetation(Ith)
Isg={s1,s2,s3,s4…..}
Isc=Scaling(Isg)
If=FeaureExtraction(Isc)
If= {f1,f2,f3,f4,…..}
Iout=Comparison_Dataset(If)
Iout={Email, Log, PDF}
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33. PROJECT PLAN
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34. IMPLEMENTATION
TILL DATE
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40. CONCLUSION
We present image processing technique which is used to generate
user’s profile. This profile will be matched with the traits of the
graphology which are already defined. The system accepts scanned
image from user and convert it to a form from which features could be
extracted and profile can be generated. After all this processing the
final output is generated and provided to the user in form of print or
email
SKNCOE, Department of
Computer Engineering 2014-15

41. Bibliography
[1] Omar Santana, Carlos M. Travieso, Jesus B. Alonso, Miguel A. Ferrer Dpto. de Senates y
COllUlnicaclones, Universldad de Las Palmas de Gran Canaria , “Writer Identification Based on Graphology
Techniques”
[2] Perut Boribalburephan and Boonnatee Sakboonyarat , “An Algorithm Development for Handwritten
Character Recognition by Personal Handwriting Identity Analysis [PHIA]”, Knowledge and Smart
Technology(KST),2012 TH International Conference, Pages 6-10.
[3] Champa H N,K R AnandaKumar, “Artificial Neural Network for human behaviour prediction through
Handwriting Analysis”INTERNATIONAL journal of computer application may 2010.
[4] Tristan Wright, “Handwriting Recognition with Artificial Neural Networks and Open CV”.
[5] Sargur N. Srihari, Ph.D.; Sung-Hyuk Cha, Ph.D.; Hina Arora, M.E.; and Sangjik Lee,M.S , “Individuality of
Handwriting”
[6] Nobuyuki Otsu (1979). "A threshold selection method from gray-level histograms". IEEE Trans. Sys.,
Man., Cyber. 9 (1): 62–66. 10.1109/TSMC.1979.4310076
[7] Vassilis Katsouros and Vassilis Papavassiliou Institute for Language and Speech Processing/R.C.
“Athena” Greece , “Segmentation of Handwritten Document Images into Text Lines”
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42. Thank you ☺