public static void ProcessImageFile(OcrReader ocrReader, string file)
{
using (Bitmap bmp = ImageFiles.LoadImageFile(file))
{
Bitmap grayscale, binary, binarySplit;
PageSections pageSections = PrepareBitmaps(bmp, out grayscale, out binary, out binarySplit);
ocrReader.ReadPage(new Bytemap(grayscale), new Bytemap(binary), new Bytemap(binarySplit), pageSections);
grayscale.Dispose();
binary.Dispose();
binarySplit.Dispose();
}
}
public static void ProcessImageFile(OcrReader ocrReader, string file, out Bitmap displayA, out Bitmap displayB)
{
using (Bitmap bmp = ImageFiles.LoadImageFile(file))
{
Bitmap grayscale, binary, binarySplit;
PageSections pageSections = PrepareBitmaps(bmp, out grayscale, out binary, out binarySplit);
displayA = new Bitmap(binary);
displayB = new Bitmap(grayscale);
AnnotatePageStructure(displayA, pageSections);
AnnotatePageHeatmap(displayB, ocrReader.QualityData);
}
}
public static bool ProcessImage(OcrReader ocrReader, Bitmap bmp, out Bitmap displayA, out Bitmap displayB)
{
Bitmap grayscale, binary, binarySplit;
bmp = bmp.Clone() as Bitmap;
PageSections pageSections = PrepareBitmaps(bmp, out grayscale, out binary, out binarySplit);
if (pageSections == null)
{
displayA = new Bitmap(binary);
displayB = new Bitmap(grayscale);
return false;
}
ocrReader.ReadPage(new Bytemap(grayscale), new Bytemap(binary), new Bytemap(binarySplit), pageSections);
displayA = new Bitmap(binary);
displayB = new Bitmap(grayscale);
AnnotatePageStructure(displayA, pageSections);
AnnotatePageHeatmap(displayB, ocrReader.QualityData);
return true;
}
// Train the neural networks.
//Note: This depends on the way your knowlwdge directory is set up and may need to be changed whenever retraining!
public static void TrainNN(out OcrReader ocrReader)
{
int dimensionX = Properties.Settings.Default.DimensionX;
int dimensionY = Properties.Settings.Default.DimensionY;
NeuralNet nnDescriptions, nnTables, nnNumbers, nnHeadlines, nnDelimiters;
List<string> knowledge = new List<string>();
List<char> netKeys = new List<char>();
string files = "abcdefghijklmnopqrstuvwy";
for (int i = 0; i < files.Length; i++)
{
knowledge.Add(PathHelpers.BuildKnowledgeFilename(DescriptionsNetwork, files[i].ToString() + "_lower"));
netKeys.Add(files[i]);
}
knowledge.Add(PathHelpers.BuildKnowledgeFilename(DescriptionsNetwork, "delimiter"));
netKeys.Add('.');
netKeys.Add('#');
nnDescriptions = new NeuralNet(dimensionX, dimensionY, netKeys, knowledge);
nnDescriptions.SaveFile = PathHelpers.BuildNetworkFilename(DescriptionsNetwork);
nnDescriptions.Train(Properties.Settings.Default.SamplesDescriptions);
knowledge.Clear();
netKeys.Clear();
files = "ABCDEFGHIJKLMNOPRSTUVWXY";
for (int i = 0; i < files.Length; i++)
{
knowledge.Add(PathHelpers.BuildKnowledgeFilename(TablesNetwork, files[i].ToString() + "_upper"));
netKeys.Add(files[i]);
}
knowledge.Add(PathHelpers.BuildKnowledgeFilename(TablesNetwork, "delimiter"));
netKeys.Add(':');
netKeys.Add('(');
netKeys.Add(')');
knowledge.Add(PathHelpers.BuildKnowledgeFilename(TablesNetwork, "minus"));
netKeys.Add('-');
nnTables = new NeuralNet(dimensionX, dimensionY, netKeys, knowledge);
nnTables.SaveFile = PathHelpers.BuildNetworkFilename(TablesNetwork);
nnTables.Train(Properties.Settings.Default.SamplesTables);
knowledge.Clear();
netKeys.Clear();
knowledge.Add(PathHelpers.BuildKnowledgeFilename(DelimitersNetwork, "comma"));
netKeys.Add('#');
knowledge.Add(PathHelpers.BuildKnowledgeFilename(DelimitersNetwork, "dot"));
netKeys.Add('.');
knowledge.Add(PathHelpers.BuildKnowledgeFilename(DelimitersNetwork, "minus"));
netKeys.Add('-');
nnDelimiters = new NeuralNet(dimensionX, dimensionY, netKeys, knowledge);
nnDelimiters.SaveFile = PathHelpers.BuildNetworkFilename(DelimitersNetwork);
nnDelimiters.Train(Properties.Settings.Default.SamplesNumbers);
knowledge.Clear();
netKeys.Clear();
files = "0123456789";
for (int i = 0; i < files.Length; i++)
{
knowledge.Add(PathHelpers.BuildKnowledgeFilename(NumbersNetwork, files[i].ToString()));
netKeys.Add(files[i]);
}
nnNumbers = new NeuralNet(dimensionX, dimensionY, netKeys, knowledge);
nnNumbers.SaveFile = PathHelpers.BuildNetworkFilename(NumbersNetwork);
nnNumbers.Train(Properties.Settings.Default.SamplesNumbers);
knowledge.Clear();
netKeys.Clear();
files = "0123456789";
for (int i = 0; i < files.Length; i++)
{
knowledge.Add(PathHelpers.BuildKnowledgeFilename(HeadlinesNetwork, files[i].ToString()));
netKeys.Add(files[i]);
}
files = "ABCDEFGHIJKLMNOPQRSTUVXYZ";
for (int i = 0; i < files.Length; i++)
{
knowledge.Add(PathHelpers.BuildKnowledgeFilename(HeadlinesNetwork, files[i].ToString() + "_upper"));
netKeys.Add(files[i]);
}
files = "abcdefghijklmnopqrstuvwyz";
for (int i = 0; i < files.Length; i++)
{
knowledge.Add(PathHelpers.BuildKnowledgeFilename(HeadlinesNetwork, files[i].ToString() + "_lower"));
netKeys.Add(files[i]);
}
knowledge.Add(PathHelpers.BuildKnowledgeFilename(HeadlinesNetwork, "minus"));
netKeys.Add('-');
nnHeadlines = new NeuralNet(15, 22, netKeys, knowledge);
nnHeadlines.SaveFile = PathHelpers.BuildNetworkFilename(HeadlinesNetwork);
nnHeadlines.Factor = 2;
nnHeadlines.Train(Properties.Settings.Default.SamplesHeadlines);
ocrReader = new OcrReader(nnDescriptions, nnTables, nnNumbers,nnHeadlines, nnDelimiters);
}
