trainOCR
Syntax
Description
Training
modelFileName = trainOCR(trainingData,modelName,baseModel,ocrOptions)ocrOptions.
[
returns a structure that contains information on training progress, such as the training
root mean squared error (RMSE) and learning rate for each iteration, using the input
arguments from the previous syntax. For a list of the returned error rates, see the modelFileName output
argument.modelFileName,info] = trainOCR(___)
Resume training
[
resumes training from an OCR training checkpoint. Use this syntax to improve the accuracy
of your OCR model by using additional training data or to perform more training
iterations.modelFileName,info] = trainOCR(trainingData,modelName,checkpoint,ocrOptions)
Examples
This example shows how to train an OCR model that can recognize fourteen-segment characters. The training data contains word samples of fourteen-segment characters.
Unzip and extract training images.
datasetZip = 'dseg14.zip';
evalfiles = unzip(datasetZip);The training images was annotated with bounding boxes containing words and text labels were added to these bounding boxes as an attribute using the Image Labeler. The labels were exported from the app as groundTruth object and saved in dseg14Gtruth.mat file.
ld = load("dseg14Gtruth.mat");
gTruth = ld.gTruth;Create datastores that contain images, bounding boxes and text labels from the groundTruth object using the ocrTrainingData function with the label and attribute names used during labeling.
labelName = "Text"; attributeName = "Word"; [imds,boxds,txtds] = ocrTrainingData(gTruth,labelName,attributeName);
Combine the datastores.
cds = combine(imds,boxds,txtds);
Split the data for training and validation with a training-to-validation ratio of 0.9
% Set the random number seed for reproducibility. rng(0); % Compute number of training and validation samples. trainingToValidationRatio = 0.9; numSamples = height(ld.gTruth.LabelData); numTrainSamples = ceil(trainingToValidationRatio*numSamples); % Divide the dataset into training and validation. indices = randperm(numSamples); trainIndices = indices(1:numTrainSamples); validationIndices = indices(numTrainSamples+1:end); cdsTrain = subset(cds, trainIndices); cdsValidation = subset(cds, validationIndices);
Specify training options. Set the gradient decay factor for ADAM optimization to 0.9, and use an initial learning rate of 40e-4. Set the verbose frequency to 160 iterations and the maximum number of epochs for training to 5. Specify the checkpoint path to enable saving checkpoints and specify the validation data to enable validation.
outputDir = "OCRModel"; if ~exist(outputDir, "dir") mkdir(outputDir); end checkpointsDir = "Checkpoints"; if ~exist(checkpointsDir, "dir") mkdir(checkpointsDir); end ocrOptions = ocrTrainingOptions(GradientDecayFactor=0.9, ... InitialLearnRate=40e-4, MaxEpochs=5, VerboseFrequency=160, ... CheckpointPath=checkpointsDir, ValidationData=cdsValidation, ... OutputLocation=outputDir);
Train a new OCR model by fine-tuning the pretrained "english" model. The training will take about 3-4 minutes.
outputModelName = "fourteenSegment"; baseModel = "english"; outputModel = trainOCR(cdsTrain, outputModelName, baseModel, ocrOptions);
************************************************************************* Starting OCR training Model Name: fourteenSegment Base Model: english Preparing training data... 100.00 % completed. Preparing validation data... 100.00 % completed. Character Set: +,-./0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ |======================================================================================================================================| | Epoch | Iteration | Time Elapsed | Training Statistics | Validation Statistics | Base Learning | | | | (hh:mm:ss) | RMSE | Character Error | Word Error | RMSE | Character Error | Word Error | Rate | |======================================================================================================================================| | 1 | 1 | 00:00:28 | 9.51 | 100.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.0040 | | 1 | 160 | 00:00:43 | 2.43 | 18.46 | 38.12 | 1.69 | 14.19 | 27.78 | 0.0040 | | 2 | 320 | 00:00:57 | 1.47 | 10.22 | 21.88 | 0.75 | 6.35 | 11.11 | 0.0040 | | 3 | 480 | 00:01:12 | 1.06 | 6.94 | 15.00 | 0.49 | 5.56 | 5.56 | 0.0040 | | 4 | 640 | 00:01:26 | 0.86 | 5.30 | 11.72 | 0.68 | 5.56 | 5.56 | 0.0040 | | 5 | 800 | 00:01:41 | 0.73 | 4.25 | 9.50 | 0.49 | 6.35 | 11.11 | 0.0040 | | 5 | 845 | 00:01:45 | 0.70 | 4.06 | 9.11 | 0.49 | 6.35 | 11.11 | 0.0040 | |======================================================================================================================================| OCR training complete. Exit condition: Reached maximum epochs. Model file name: OCRModel/fourteenSegment.traineddata *************************************************************************
Use the trained model to perform OCR on a test image and visualize the results.
I = imread("DSEG14.png"); ocrResults = ocr(I,Model=outputModel); Iocr = insertObjectAnnotation(I,"rectangle",... ocrResults.WordBoundingBoxes,ocrResults.Words,... LineWidth=2,FontSize=17); imshow(Iocr)
Input Arguments
Output Arguments
Limitations
Training OCR models with right-to-left scripts such as Arabic and Hebrew are not supported.
Algorithms
The
trainOCRfunction creates a temporary folder, "<modelName>Training/, where <modelName> is the value of themodelNameargument, in the location specified by theOutputLocationproperty of theocrTrainingOptionsobject. The folder contains training artifacts. If the folder does not already exist before you run thetrainOCRfunction, the function deletes it at the end of training. If the folder already exists prior to training, the function does not delete the folder.Images read from
trainingDatamust contain text of at least one-word length and up to a maximum of one-line length. ThetrainOCRfunction does not support images that contain multiple lines of text.The
trainOCRfunction does not support on-the-fly data augmentation using a datastore transform. All the image data is read once from the training datastores at the start of training.
