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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>train_ocr.m</title><link rel="stylesheet" type="text/css" href="../../../m-syntax.css"></head><body><code><span class=h1>% TRAIN_OCR Training of OCR classifier based on multiclass SVM.
</span><br><span class=help>%
</span><br><span class=help>% <span class=help_field>Description:</span></span><br><span class=help>% The following steps are performed:
</span><br><span class=help>% - Training set is created from data in directory ExamplesDir.
</span><br><span class=help>% - Multi-class SVM is trained.
</span><br><span class=help>% - The trained SVM model is saved.
</span><br><span class=help>%
</span><br><hr><br><span class=help1>% <span class=help1_field>(c)</span> Statistical Pattern Recognition Toolbox, (C) 1999-2003,
</span><br><span class=help1>% Written by Vojtech Franc and Vaclav Hlavac,
</span><br><span class=help1>% <a href="http://www.cvut.cz">Czech Technical University Prague</a>,
</span><br><span class=help1>% <a href="http://www.feld.cvut.cz">Faculty of Electrical engineering</a>,
</span><br><span class=help1>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>
</span><br><br><span class=help1>% <span class=help1_field>Modifications:</span>
</span><br><span class=help1>% 4-jun-2004, VF
</span><br><span class=help1>% 9-sep-2003, VF
</span><br><br><hr><span class=comment>% Setting
</span><br><span class=comment>%===================================
</span><br>ExamplesDir = <span class=quotes>'../../data/ocr_numerals/'</span>; <span class=comment>% input directory with examples
</span><br>OCRFileName = <span class=quotes>'ocrmodel'</span>; <span class=comment>% output SVM model
</span><br>
<br><span class=comment>% Model setting for multi-class SVM
</span><br>options.ker = <span class=quotes>'rbf'</span>; <span class=comment>% kernel type
</span><br>options.arg = 5; <span class=comment>% kernel argument
</span><br>options.C = [inf]; <span class=comment>% regularization constant
</span><br>options.verb = 100; <span class=comment>% display progress info
</span><br>
<br><span class=comment>%options.solver ='svmlight';
</span><br>options.solver =<span class=quotes>'smo'</span>; <span class=comment>% use if SVM^{light} is not installed
</span><br>
<br><span class=comment>% Create training set
</span><br><span class=comment>%====================================
</span><br><span class=io>fprintf</span>(<span class=quotes>'Creating training set:\n'</span>);
<br>TrainingDataFile = [ExamplesDir <span class=quotes>'OcrTrndata.mat'</span>];
<br>mergesets( ExamplesDir, TrainingDataFile );
<br>data = load(TrainingDataFile );
<br>
<br><span class=comment>% Training SVM model
</span><br><span class=comment>%====================================
</span><br>
<br><span class=io>fprintf</span>(<span class=quotes>'Training multi-class SVM classifier.\n'</span>);
<br><span class=comment>%model = oaosvm(data,options);
</span><br>
<br><span class=comment>% Multi-class BSVM with L2-soft margin can be asls used
</span><br>options.solver = <span class=quotes>'imdm'</span>;
<br>model = bsvm2(data,options);
<br>
<br><span class=comment>% One-Against-All decomposition can be also used
</span><br><span class=comment>%options.solver = 'svmlight';
</span><br><span class=comment>%model = oaasvm(data,options);
</span><br>
<br>
<br><span class=comment>% mapping class label y -> character
</span><br>model.labels = [<span class=quotes>'1'</span> <span class=quotes>'2'</span> <span class=quotes>'3'</span> <span class=quotes>'4'</span> <span class=quotes>'5'</span> <span class=quotes>'6'</span> <span class=quotes>'7'</span> <span class=quotes>'8'</span> <span class=quotes>'9'</span> <span class=quotes>'0'</span>];
<br>
<br><span class=io>fprintf</span>(<span class=quotes>'Saving found classifier to file %s...'</span>, OCRFileName);
<br>savestruct(model,OCRFileName);
<br><span class=io>fprintf</span>(<span class=quotes>'done.\n'</span>);
<br>
<br><span class=comment>% EOF</span><br></code>⌨️ 快捷键说明
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