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Before you can use a classifier, train and test it with examples of real images. Training is based on the reference classes specified in the classifier’s settings. To train the classifier, set each document’s state, split the batch into training and testing sets, and then launch the training.

Document states

During training, each document is in one of the following states: To see a document’s state, switch to thumbnail view with the View Thumbnails button on the toolbar.

Split documents for training and testing

Before launching training, move some documents to the For Testing state. This lets you analyze the classification results afterward and improve the classifier. You can split the batch automatically or manually.

Split automatically

1

Open the Benchmark dialog box

Click the Benchmark button on the toolbar, or select Classification Training → Benchmark.
2

Set the split

Specify the percentage of documents to use for training and for testing. Optionally, set a minimum number of training documents per class (the default is 1).
3

Run the test or split only

To split the documents and start training, select Run benchmark test and click OK. To only assign document states and keep setting up the classifier, select Only split documents and click OK.

Split manually

Select the documents and click Set Document State in the shortcut menu or the Classification Training menu.

Launch training

After setting up the classifier, launch training in one of the following ways:
  • Click the Train button on the toolbar.
  • Select Classification Training → Train.
  • Select Train in the shortcut menu.
You can classify any page regardless of its assigned state: select the pages and click Classify on the toolbar or in the Classification Training menu. Use this to assign reference classes based on classification, or to test the classifier on particular pages.

Classification result colors

FlexiCapture highlights the names of the resulting and reference classes (or the absence of both) with the classification-results color: After testing the classifier with the test batch, analyze the classification statistics.