Handwriting Recognition

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					Handwriting Recognition

While the keyboard is the traditional means for entering text into a computer system, both designers
and users have long acknowledged the potential benefits of a system where people could enter text
using ordinary script or printed handwriting and have it converted to standard computer character
codes. With such a system people would not need to master a typewriter-style keyboard. Further, users
could write commands or take notes on handheld or “palm” computers the size of a small note pad that
are too small to have a keyboard. Indeed, such facilities are available to a limited extent today.

A handwriting recognition system begins by building a representation of the user’s writing. With a pen
or stylus system, this representation is not simply a graphical image but includes the recorded “strokes”
or discrete movements that make up the letters. The software must then create a representation of
features of the handwriting that can be used to match it to the appropriate character templates.
Handwriting recognition is actually an application of the larger problem of identifying the significance of
features in a pattern. One approach (often used on systems that work from previously written
documents rather than stylus strokes) is to identify patterns of pixels that have a high statistical
correlation to the presence of a particular letter in the rectangular “frame” under consideration.
Another approach is to try to identify groups of strokes or segments that can be associated with
particular letters. In evaluating such tentative recognitions, programs can also incorporate a network of
“recognizers” that receive feedback on the basis of their accuracy. Finally, where the identity of a letter
remains ambiguous, lexical analysis can be used to determine the most probable letter in a given
context, using a dictionary or a table of letter group frequencies.

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