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    <p>I don't have an algorithm to give you the key features, but here are some things that might help.</p> <p>First, I wouldn't worry too much about looking for a characteristic pixel for each character because, on the average, checking if a given character matches with a given swath (5x5) of the binary image shouldn't take more than 5-7 checks to tell that the there isn't a match. Why? Probability. For 7 binary pixels, there are 2**7=128 different possibilities. That means there is a 1/128 &lt; 1% chance of a character matching even up to 7 pixels. Just make sure that you stop the comparisons right when you find a mismatch.</p> <p>Second, if you don't want to do a hash table, then you might consider using a <a href="http://en.wikipedia.org/wiki/Trie" rel="nofollow noreferrer">trie</a> to store all of your character data. It will use less memory, and you'll be checking all of the characters at once. It won't be quite as fast to search through as a hash table, but you also won't have to convert to a string. At each node in the tree, there can only be at most 2 descendants. For instance, if you have two 2x2 characters (let's call them A and B):</p> <pre><code>A B 01 00 10 11 </code></pre> <p>You trie would have only one descendant at the first node - only to the left(the 0 branch). We proceed to this next node. It has two descendents, the left (0) branch leads to the rest of B and the right (1) branch leads to the rest of A. You get the picture. Let me know if this part isn't clear.</p>
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    1. POOptimized OCR black/white pixel algorithm
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    1. COSorry I wasn't very clear about the 10x11 and 5x5. 10x11 is the actual dimensions of a character in my program. 5x5 is my simplified version in this post. I clarified it in the posting on top as well.
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    2. COI took that part out.
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    3. COThe trie makes sense. It would be even more beneficial to use a compressed trie.
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