Sliding Window: A common way of breaking up features in an image, such as characters or words, is to use a Sliding Window to "cut out" parts of the picture, which are then compared against previously trained sample data of good places to split characters vs bad places. The window is then moved by some step size and that is repeated all over the image. Then that is repeated again with a slightly larger window. This is very costly, but works well.
Outline Tracing: Joseph Watson shared
"an algorithm used by a simple system intended to read books. In this system, the idea was to explore an image consisting of only black and white pixels to determine what printed letters might be found there. Needless to say, the pixels were much smaller than the printed letters.
The first part of the algorithm consists simply of scanning around in the white image until a black pixel is found. Then the following rules are applied with each successive pixel check. Each move is made by moving only in X or in Y, never along a diagonal.
1. If you have turned the same way three times in a row, turn the other way.
2. If the sampled pixel is black, turn right.
3. If the sampled pixel is white, turn left.
What this algorithm accomplishes is that it traces around the perimeter of the black character shape. When the trace action around a character begins to retrace the same pixels, it is terminated, the character was then recognized by the maximum and minimum points reached in the X and Y axes and the order in which they are found."
Objectness Measures: Attempts to find general areas where an object of any type is likely vs areas of background, noise, etc... This then guides the use of sliding windows and systems trained to recognize a specific type of object.
This is done through a series of test, taking cues of different types from the image, which are combined in a Bayesian framework
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<A HREF="http://massmind.org/techref/method/ai/imagebreakup.htm"> Image Recognition Methods for Breaking out Features</A>
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