Handwriting Recognition - Off-line Recognition - Character Recognition - Feature Extraction

Feature Extraction

Feature extraction works in a similar fashion to neural network recognizers however, programmers must manually determine the properties they feel are important.

Some example properties might be:

• Aspect Ratio
• Percent of pixels above horizontal half point
• Percent of pixels to right of vertical half point
• Number of strokes
• Average distance from image center
• Is reflected y axis
• Is reflected x axis

This approach gives the recognizer more control over the properties used in identification. Yet any system using this approach requires substantially more development time than a neural network because the properties are not learned automatically.