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

Partnership for Research in Spatial Modeling (PRISM)

Dr. Anshuman Razdan

Director

(razdan@asu.edu)

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Parsing the OCR Problem

• Preprocessing and Image enhancement

• Pen Stroke Creation

• Character recognition

• Word recognition

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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.

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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

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Enhancement & Thresholding

Original image Enhanced image

Line extraction by thresholding

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Spanish Manuscript Example

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Why 3D Analysis?

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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

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An Example

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

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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….

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3D Pen Traces

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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

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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.

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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

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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))

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

volumetric data such as MRI, CAT scan, etc.

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MC: Thresholding• Explanation of how Marching Cubes uses predefined

triangulations for each cube to form a whole mesh.

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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.

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Modern Writing

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Demo of Current Implementation

Curve Shape Measures and Matching for Character Recognition

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• 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

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Curve Matching Applied to Chars (Demo)

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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.

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Partial Match

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Best Match

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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.

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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

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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.

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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

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Discussion and Q/A

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Appendix

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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

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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

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3 characteristics (Chaudhuri et al)

1. Piecewise linear segments

2. Cross section as a Gaussian function

3. Relatively constant width

Image Refinement• Blood Vessel

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2D Line Model

(x,y))sin,(cos v

2

2

2

sincosexp),(

yx

yxF

Blood Vessel

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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

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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

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Results

A synthetic imageCrest lines extraction

Matched filters Our method

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Applications of Curve Matching

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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

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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

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Curve Matching

• Remember • Writing in terms of

curvatures • What about partial

match?

• Or the general case

04/19/23 45

Three Matching Mesaures