Loop Investigation for Cursive Handwriting Processing and Recognition

By Tal Steinherz

Advanced Seminar (Spring 05)

Outline Background on cursive handwriting Introduction to loops

Pattern recognition and machine learning conflicts

Feature extraction solutions

Demonstrations and experimental results

Cursive Handwriting (J. C. Simon)

“Displacing a pen from left to right in an oscillating movement, with loops, descendants (legs), and ascendants (poles).”

Cursive vs. Character

Cursive – continuous concatenated set of strokes.produced by a human being in a free style.

Character – a single standalone symbol.produced by a machine subjected to numerous alternative fonts.

Online vs. Offline

Online – captured by pen-like devices.the input format is a two-dimensional signal of pixel locations as a function of time (x(t),y(t)).

Offline – captured by scanning devices.the input format is a two-dimensional image of gray-scale colors as a function of location I(m*n).strokes have significant width.

Online vs. Offline (demo)

A Loop (T. Steinherz)

“A set of neighboring foreground pixels surrounding a hole, i.e., a connected blocked group of background pixels in the word’s image, where all foreground pixels are within stroke width distance from the hole.”

Ascending (Descending) Loops

Axial (of the middle zone) Loops

The importance of loops Shared by many letters (especially

a,d,e,g,o,p,q) Byproduct of the continuous nature

of cursive handwriting (like with b,f,h,j,k,l,s,t,y,z)

Elementary and prominent features Carry additional information given

by a set of descriptive parameters

The motivation to investigate loops Character recognition

supports discrimination between letters.

Writer modeling Identification Examination

contributes to applications in forensic science and graphology.

The output of loop investigation Incomplete (open) loop identification Hidden (collapsed) loop tracking -

locating blobs that correspond to online loops

Multi (encapsulated) loops understanding - distinguishing natural from artificial loops

Temporal information recovery - retracing the original path of a pen

The Engineering Approach(J. C. Simon & T. Pavlidis)

“Requires understanding the structure of the objects to be recognized and apply the appropriate combination of (pattern recognition) techniques.”

Feature extraction dilemmas

Offline cursive word signal representation Loop identification Signal to noise ratio Feature vector translation

The difficulties consist in the feature extraction and preprocessing rather than the machine learning \ recognition engine phase.

Offline cursive word signal representation

We use the external upper and lower contours in conjunction with the internal contour of all visible loops.

Loop identification

Given a set of singular points, identification is provided by correlation between pieces of the same contour (around anchor points), of the opposite contours and\or in association with subsets of internal contours.

Signal to noise ratio

In order to improve the signal’s parametric quantifiability and reduce noisy artifacts, the contour is transformed to a polygon.

Hidden loop tracking -the mutual distance principle

Hidden loop tracking -the mutual distance principle (cont.)

Hidden loop tracking -the mutual distance principle (cont.)

Multi loops understanding -the continuity principle

Temporal information recovery -the matching principle

Hidden loop tracking -an application to ascending (descending) loops

Writer#1 Writer#2 Writer#3 Writer#4 Writer#5 Writer#6 Total

Number of words

Number of characters

Number of Loops (all kinds)

223 219 223 170 215 223

1130 1113 1130 835 1083 1130

1273

6421

1039 1272 1013 745 1332 1146 6547

Hidden loop tracking -an application to ascending (descending) loops (cont.)

Offline Loops

Encapsulated Disqualified Found Total

Online LoopsReal Loops

Number

Rate

259

100%

1006 186 519 964

25.7% 18.5% 51.6% 95.8%

Hidden loop tracking -an application to ascending (descending) loops (cont.)

Offline Loops

Encapsulated Disqualified Found Total

Online LoopsLarge Loops

(8)<

Number

Rate

233

100%

856 147 341 721

27.2% 17.2% 39.8% 84.2%

Offline Loops

Encapsulated Disqualified Found Total

Online LoopsLarge Loops

(6)<

Number

Rate

288

100%

1105 177 390 855

26.1% 16.0% 35.3% 77.4%

Hidden loop tracking -an application to ascending (descending) loops (cont.)

Threshold Small Loops No Loops Total

8 180 209 389

6 131 209 340

Multi loops understanding -a classifier of beginning a-s

More than 40 writers with 1-4 samples per writer.

Multi loops understanding -a classifier of beginning a-s

Type A Type B Error QuestionableTotal Loops

Number

Rate

32/36

100%

81/93 26/28 16/21 7/8

39%/38% 30%/32% 19%/22% 7.5%/8%