Thus, a Kohonen cell is characterized not only by its feature vector
representing a single stroke, but also by a number of additional attributes,
such as the probability p*i* that cell *i* gets a 'hit', and a list
of possible stroke interpretations and their likelihood.
The concept of **stroke interpretation** can be explained as follows.
In a stroke interpretation code *Zn/m*, *Z* represents a letter,
*n* is an integer representing the nth stroke position within the
letter, and *m* indicates that this is an *m*-stroked letter.
Thus *a1/3* can be read as: "the first stroke of a three-stroked *a*".
Figure 1 displays a part of the Kohonen SOM of strokes and the attribute
lists of a number of cells (*i*, *j* and *k*).

**
Figure 1. A transition network through a Kohonen self-organized map of velocity-based strokes
**

The transition network is represented by the dotted green arrows
in Figure 1. A path of consistent stroke interpretations can be followed,
going from cell *i* to cell *j*. Two stroke interpretations
are consistent, if they refer to the same character, and if the second
stroke is the logical follow-up of the first. So, the stroke transition
*a1/3 --> a2/3* is logically consistent, whereas the transition
*d1/3 --> a2/3* is not. When a sequence of stroke interpretations
can be followed which represents a full character, a
character hypothesis (*Z*/m*) is emitted.
This simple setup makes no use of an actual
matrix of transition probabilities, which would be too expensive.
The probabilities of a sequence of stroke interpretations representing
a full character are combined to yield a quality measure of the
resulting character hypothesis. A number of schemes can be envisaged.
The product of probabilities for instance, yields low recognition rate
results. This is due to the fact that a single, badly-written stroke
overrules the good quality of the well-written strokes in such a product-rule
approach. Surprisingly, the - theoretically flimsy - average
probability ((1/*m*) Sum *p*)
already yields good results, whereas the best results are obtained
with ((1/*m*) Sum -*p*log*p*).

Please refer to our **Publications**
when using anything from the shown material.

to the "NICI stroke-based recognizer of on-line handwriting" page

- Handwriting Recognition and Document Analysis Conferences

- Pen & Mobile Computing

- NICI Handwriting Recognition Group home page

- UNIPEN tools

- Handwriting-related Java demos

Copyright Lambert Schomaker (April 1, 1996)

since 2/Oct/1996