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.
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