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3D Handwriting Analysis A. Razdan, J. Femiani, J. Rowe Partnership for Research in Spatial Modeling (PRISM) Dr. Anshuman Razdan Director ([email protected])
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Dr. Anshuman Razdan Director (razdan@asu)

Dec 31, 2015

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3D Handwriting Analysis A. Razdan, J. Femiani, J. Rowe Partnership for Research in Spatial Modeling (PRISM). Dr. Anshuman Razdan Director ([email protected]). Parsing the OCR Problem. Preprocessing and Image enhancement Pen Stroke Creation Character recognition Word recognition. - PowerPoint PPT Presentation
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Page 1: Dr. Anshuman Razdan Director (razdan@asu)

3D Handwriting AnalysisA. Razdan, J. Femiani, J. Rowe

Partnership for Research in Spatial Modeling (PRISM)

Dr. Anshuman Razdan

Director

([email protected])

Page 2: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 2

Parsing the OCR Problem

• Preprocessing and Image enhancement

• Pen Stroke Creation

• Character recognition

• Word recognition

Page 3: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 3

Image Enhancement• Preprocessing includes enhancing and refining the

raw image.• Identifying and extracting blurred, stained, faded,

bled through, or transferred characters, etc.• New PRISM method specifically identifies and

analyzes linear structures (line strokes). • This technique works in both 3D (CT, MRI) and 2D

(images) domains.

Page 4: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 4

Image Refinement

• 1D and 2D function models based on the 3 observed shape characteristics have been developed, and enhanced images are derived from their second derivatives.

• A two-stage algorithm is developed to extract line and net patterns. Line and net patterns are first enhanced and then extracted by applying threshold value.

• Line and net patterns in a noisy environment exist in many imaging technologies

• Examples: Roads and rivers in satellite photos, curves in finger prints, blood vessels in CT angiography

Page 5: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 5

Enhancement & Thresholding

Original image Enhanced image

Line extraction by thresholding

Page 6: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 6

Spanish Manuscript Example

Page 7: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 7

Why 3D Analysis?

Page 8: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 8

Flat Land: A Romance of Many Dimensions

• You have to view the problem in at least one dimension higher than the data to get a sense of it(Flatland: A Romance of Many Dimensions: by Edwin A. Abbott, A Square, circa. 1884)

KING of 1D LandObserver in 2D Land

You are in 3D looking down at 2D space

woman

High Priest

Page 9: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 9

An Example

Page 10: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 10

Now I See Now I Don’tPRISM KGL Mesh Viewer ControlC:\RazdanData\Prism\KDI\Presentations/tub_mesh_connected.kgl

Page 11: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 11

Flat Land Conclusion

• 1D (line) embed in 2D space (paper surface)

• 2D (images) embed in 3D space (like this room)

• 3D (objects) embedded in 4D or 5D space ….

• Given this argument, using 3D space for understanding 2D images makes sense….

Page 12: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 12

3D Pen Traces

Page 13: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 13

3D Pen Trace Recreation

• Concept of raising or embedding 2D image in 3D space a.k.a Flat Land.

• Understanding ink flow and information embedded in the pen strokes

• Theory of Volume Modeling and Iso-surface Extraction

Page 14: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 14

Chain Codes or Pen Traces• For any character

matching/recognition algorithm to work efficiently it needs to unravel the stroking of the pen.

• This means figuring out the chain code. Since it is not available in 2D bitmap we do it using 3D.

Page 15: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 15

Pen Stroking• Pressure is applied to via the pen and is different in

upstrokes and down strokes and also angle of writing.• There is flow of ink from the pen to the paper.

Crossovers result in darker images

Page 16: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 16

How 2D is raised to 3D

2D ImageTransformed into 3D

• A transfer function is applied which converts intensity at each pixel into a height function and also a density function

• Results in Volumetric data same as CT or MRI

H(i,j) = F(x,y, I(x,y))

D(i,j,k) = I(x,y)

Vol Func(x,y,H(i,j)) = D(I(x,y))

Page 17: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 17

Marching Cubes• Marching cubes is used for making 3D surfaces from

volumetric data such as MRI, CAT scan, etc.

Page 18: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 18

MC: Thresholding• Explanation of how Marching Cubes uses predefined

triangulations for each cube to form a whole mesh.

Page 19: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 19

Volume Blurring• Start with Volume Function (V) on raw image (left image)• Apply Marching Cubes on V (middle image)• Create V’ = GnV (Blurring filter applied n times and then MC to

create right image). Gn is the secret sauce.

Page 20: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 20

Modern Writing

Page 21: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 21

Demo of Current Implementation

Page 22: Dr. Anshuman Razdan Director (razdan@asu)

Curve Shape Measures and Matching for Character Recognition

Page 23: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 23

• Given two curves X1 and X2, one can ask two distinct questions:– Curve matching i.e.

• Is X1 = X2 ?

• Or one a subset of the other curve

• Or how similar are the two curves?

– Curve alignment i.e.• What is the rotation and translation required to align one

curve with the other?

The Problem

Page 24: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 24

Curve Matching Applied to Chars (Demo)

Page 25: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 25

Conclusions• Novel method to unravel strokes, characters and letterforms in

complex handwritten documents. • Segments by Region/Row irrespective of scale, orientation, or

position.• Geometry based curve matching technique for character

recognition (dictionary generation, text recognition, and translation)

• Language independence• Doesn’t need expensive scanning equipment (we paid $24.99).• Can be combined with existing technologies.• Provisional Patent filed in April 2003. Full patent filing spring

2004.

Page 26: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 26

Partial Match

Page 27: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 27

Best Match

Page 28: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 28

Weaknesses

• Requires continuous tone original source (can not address single bit image i.e. FAX).

• Can be computationally expensive for certain applications such as forgery but the technology is built to take advantage of parallelization.

Page 29: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 29

Opportunities• Extend concept of volumes to other applications

– Forensics (Offline comparisons)– Biometrics (Online authentication – wacom demo)– Forgery detection– Number extraction from noisy background (Currencies)

• Opportunities for derivative patents

Page 30: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 30

Gaps

• Need to combine power of Stroke extraction and curve matching with traditional HMM and other statistical methods or commercial engines.

• Man power/expertise required– AI/Statistics/traditional char recognition expert to create

powerful hybrid engine

– Language specific expert/paleographer

• Requires productization and field testing.

Page 31: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 31

Threats

• Competition by 2D solutions and existing technologies.

• Lack of awareness of the capabilities of 3D analytical tools in OCR world.– Geometry solution in a world seeped in statistical methods.

• Establishing validity of the 2D - 3D conversion algorithm

Page 32: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 32

Discussion and Q/A

Page 33: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 33

Appendix

Page 34: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 34

PRISM Infrastructure

• Two labs on campus – 0ne moving to bigger space in BY – downtown Tempe.

– Additional 8000 sq ft slated for a new project (Decision Theatre) in downtown Tempe.

• 24 proc SGI, 20+ workstations (Unix, PC and Linux)• Four 3D Laser scanners for inanimate objects• 3D face scanner (recent acquisition)• 2 Rapid Prototyping machines

Page 35: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 35

Image Refinement

• Biomedical Examples: White matter in brain MRI scans, cell spindle fibers, membranes in laser confocal microscopic data.

Brain MRI Scan Mouse egg

Fungus membrane

Page 36: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 36

3 characteristics (Chaudhuri et al)

1. Piecewise linear segments

2. Cross section as a Gaussian function

3. Relatively constant width

Image Refinement• Blood Vessel

Page 37: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 37

2D Line Model

(x,y))sin,(cos v

2

2

2

sincosexp),(

yx

yxF

Blood Vessel

Page 38: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 38

2D Case: 2nd Derivatives

),(2

)sincos(exp)sincos(

cos

2

)sincos(exp

cos),(

2

22

4

2

2

2

2

2

yxNyx

yx

yxyxF

xx

xx

),(2

)sincos(exp)sincos(

sincos

2

)sincos(exp

sincos),(),(

2

22

4

2

2

2

yxNyx

yx

yxyxFyxF

xy

yxxy

),(2

)sincos(exp)sincos(

sin

2

)sincos(exp

sin),(

2

22

4

2

2

2

2

2

yxNyx

yx

yxyxF

yy

yy

),(2

)sincos(exp),(

2

2

yxNCyx

yxF

C: constant, N: noise

Page 39: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 39

Enhancement• Maximal eigenvalue as an enhanced image

0),( if 0

0),( if ),(),(

2

yx

yxyxyxF

vv

Hv

),(1

),(

sin

cos

2

)sincos(exp

1

sin

cos

sincossin

cossincos

2

)sincos(exp

1

)sincos if ( sin

cos

2

2

2

2

2

2

2

2

2

yxFyx

yx

yx

yxFF

FF

yyyx

xyxx

Enhanced Image

Page 40: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 40

Results

A synthetic imageCrest lines extraction

Matched filters Our method

Page 41: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 41

Applications of Curve Matching

Page 42: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 42

Distance Between Two Functions

Penalty function

Case 1: f and g continuous over [0,1]

Case 2: f over [0,1] and g over [0,d], d <= 1

Page 43: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 43

Curve Shape Measures• Shape Measures or Properties

– Curvature (planar)– Torsion (space curves)– Total or absolute Curvature (space)

• Classical Differential geometry says if the curvatures are identical then so are the curves subject to position and rotation

Page 44: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 44

Curve Matching

• Remember • Writing in terms of

curvatures • What about partial

match?

• Or the general case

Page 45: Dr. Anshuman Razdan Director (razdan@asu)

04/19/23 45

Three Matching Mesaures