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@@ -181,7 +181,7 @@ which vectors to associate with a character.
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\end{enumerate}
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\end{enumerate}
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-To our knowledge, LBP has yet not been used in this manner before. Therefore,
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+To our knowledge, LBP has not yet been used in this manner before. Therefore,
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it will be the first thing to implement, to see if it lives up to the
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it will be the first thing to implement, to see if it lives up to the
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expectations. When the proof of concept is there, it can be used in a final,
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expectations. When the proof of concept is there, it can be used in a final,
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more efficient program.
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more efficient program.
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@@ -194,7 +194,7 @@ working with just one cell) gives us the best results.
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Given the LBP of a character, a Support Vector Machine can be used to classify
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Given the LBP of a character, a Support Vector Machine can be used to classify
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the character to a character in a learning set. The SVM uses the concatenation
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the character to a character in a learning set. The SVM uses the concatenation
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of the histograms of all cells in an image as a feature vector (in the case we
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of the histograms of all cells in an image as a feature vector (in the case we
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-check the entire image no concatenation has to be done of course. The SVM can
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+check the entire image no concatenation has to be done of course). The SVM can
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be trained with a subset of the given dataset called the ``learning set''. Once
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be trained with a subset of the given dataset called the ``learning set''. Once
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trained, the entire classifier can be saved as a Pickle object\footnote{See
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trained, the entire classifier can be saved as a Pickle object\footnote{See
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\url{http://docs.python.org/library/pickle.html}} for later usage.
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\url{http://docs.python.org/library/pickle.html}} for later usage.
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Taddeus Kroes