CALI: An Online Scribble Recognizer for Calligraphic Interfaces - PowerPoint PPT Presentation

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CALI: An Online Scribble Recognizer for Calligraphic Interfaces

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Alternative user interface based on scribble recognition. Recognition of basic shapes (such as triangle, rectangles, circles) and basic ... Na ve Bays training ... – PowerPoint PPT presentation

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Title: CALI: An Online Scribble Recognizer for Calligraphic Interfaces


1
CALI An Online Scribble Recognizer for
Calligraphic Interfaces
  • By
  • Manuel J. Fonseca
  • Cesar Pimentel
  • Joaqui, A. Jorge

11 November 2002
2
Introduction
  • Alternative user interface based on scribble
    recognition
  • Recognition of basic shapes (such as triangle,
    rectangles, circles) and basic commands (such as
    copy and delete)
  • More robust than others, trainable, multi-stroke

Recognizable Shapes
3
Non-trainable Recognizer
  • Global geometric properties
  • Filters for identification of shapes and removal
    of unwanted shapes
  • Fuzzy logic for uncertainty in sketches
  • Strokes ? Scribbles ? Shapes
  • Return a list of plausible shapes ordered by
    degree of certainty

4
Geometric features (non-train.)
  • Compute the convex hull of shapes points
  • Compute largest area triangle, quadrilateral
    inscribed in the convex hull and smallest area
    enclosing rectangle, area and perimeter
  • Compute percentiles of distributions of feature
    values for each shape (class)

Polygons
Percentiles
5
Deriving Fuzzy Sets
  • Each shape is defined by several fuzzy sets
  • E.g. thinness for lines
  • For each feature there is a trapezoidal fuzzy set
    with 4 values
  • All design of fuzzy sets is done manually

Fuzzy Sets
6
Recognition Rules
  • If Scribble is very thin, then shape is a line
  • If A(lt)/A(lq) is like diamond and
  • A(lq)/A(ch) is not like Ellipse and
  • A(lq)/A(er) is not like Bold Line and
  • A(lq)/A(er) is not like rectangle
  • Then shape is a diamond
  • Line style is identified after basic shapes

7
Re-segmentation
  • Some shapes cannot be recognized based entirely
    on global geometric properties
  • E.g. Arrows and Crosses
  • If strokes gt 2 and
  • Last stroke is like triangle or
  • Last stroke is like move
  • Then shape is an arrow
  • If strokes 2 and
  • First stroke is like line and
  • Second stroke is like line
  • And first stroke intersects second stroke
  • Then shape is a cross

Arrows
8
Ambiguity
  • Return a list of plausible candidates when in
    doubt, ordered by degree of membership

Ambiguity
Fuzzy Sets
9
Trainable Recognizer
  • Need for new gesture (shape) specification
  • Naïve Bays training
  • KNN should be better after implementation of
    indexing structure for high-D data (in the works)

10
Results
  • Non-train. about 50 ms on a Pentium II
  • 95.8 recognition rate
  • Train. 93.3

Confusion Matrix
11
Thank you for your attention
  • Have a nice evening...

12
Shapes Identified by CALI
13
Polygons used to estimate features
14
Percentiles of feature values
  • H(er)/W(er) - aspect ratio
  • Bars are for 25-75
  • Lines are for 10-90
  • Line (small)
  • Per(ch)2/A(ch) - thinness
  • Bars are for 25-75
  • Lines are for 10-90
  • Circle (small but visible)
  • Lines (so small thats invisible)

15
Fuzzy Sets
  • b and c correspond to 10 and 90 percentiles
  • a and b are for min and max after clearing out
    outliers

16
Different types of arrows
17
Ambiguity cases among shapes
18
Fuzzy sets for ambiguity
19
Confusion Matrix for non-train.
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