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.)

Multi loops understanding - the continuity principle

Temporal information recovery -the matching principle

Hidden loop tracking - an application to ascending (descending) loops Writer#1Writer#2Writer#3Writer#4Writer#5Writer#6Total Number of words Number of characters Number of Loops (all kinds)

Hidden loop tracking - an application to ascending (descending) loops (cont.) Offline Loops Encapsulate d DisqualifiedFoundTotal Online Loops Real Loops Number Rate % %18.5%51.6%95.8%

Hidden loop tracking - an application to ascending (descending) loops (cont.) Offline Loops Encapsulate d DisqualifiedFoundTotal Online Loops Large Loops (8<) Number Rate % %17.2%39.8%84.2% Offline Loops Encapsulate d DisqualifiedFoundTotal Online Loops Large Loops (6<) Number Rate % %16.0%35.3%77.4%

Hidden loop tracking - an application to ascending (descending) loops (cont.) Threshol d Small Loops No Loops Total

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 AType BError Questionabl e Total Loops Number Rate 32/36 100% 81/9326/2816/217/8 39%/38%30%/32%19%/22%7.5%/8%