# Train the Tool

When using the Blue Read to identify characters, follow these steps:

1. Once a labeled instance of each character has been completed, it's time to train the tool. To train the tool, first add the desired images to the training set.

2. Select the images in the View Browser, and on the right-click pop-up menu click Add views to training set.

   1. To select multiple images in the View Browser, use the Shift + Left Mouse Button. Or, use Display Filters to show the desired images for the training only and add them to the training set by clicking Actions for ... views → Add views to training set.

3. Prior to training, you need to set parameters in Tool Parameters. You can configure Training, Sampling, and Perturbation parameters or just use the default values of these. See Blue Read Tool Parameters for the details of the supported parameters.

   1. Ensure that the Feature Size parameter in Sampling parameters has been set. The Feature Size parameter gives the network a clue to the size of the characters that you are interested in identifying. So, if the Feature Size parameter setting is clearly going off from the size of characters in your application, there is a chance the tool will not identify the characters in your image.

   2. If you want more granular control over training or processing, turn on Expert Mode on Help menu to initialize additional parameters in Tool Parameters.

4. Once the training set is filled with the right images, press the Brain icon, and the software will begin its computation for the training.

   1. If you stop training in the middle of it by pressing the Stop icon, you can stop training but you will lose the current tool so far trained.

5. After training, review the results. Open Database Overview panel and review the Confusion Matrix and Precision, Recall, F-Score for each character and each model to understand the results. See Blue Read Tool – Statistics for the details of interpreting results.

6. After reviewing the results, go through all of the images and see how the tool correctly or incorrectly marked the features and models for each image.

### Tips and Tricks for Training

The labeled instances will be used during the deep learning phase of the tool's processing via the neural network. It's not necessary to label multiple instances of each character, but the more labeled instances there are, the better the results will be during the training phase. Training will increase the accuracy of the tool. The more you train, the more you tune your neural network towards your particular training set. The tool starts with a very generalized model, and every time you train, the tool is going to key in more and more to what you have in your training set. So, if your training set isn't a very good representation of what you'll encounter at run time, you would be better off to minimize the amount of training you'll do.

If you have labeled everything, and the labeled features represent the expected appearances (and preferably involve the instances of each character), training will improve the performance of the tool. However, if you have an unrepresentative set of images (for example, characters on a different background or a slant that isn't expected at runtime), training may narrow the performance to the unrepresentative set of characters, so training isn't advised.

To have a successful implementation of the tool, you need to have a set of images that contain the full range of expected variations of the characters at runtime.

Important tool parameters involved in training:

- Train Selection: Defines the percentage of labeled images that will be used during training. By default, 10% of the labeled images will be used during training; the other 90% are unlabeled images.
- Keep checkbox: Defines that you will reuse the same labeled images during each subsequent training.

Tip: If you have images that you always want to be included in the training, after the View is accepted, you can right-click the image, and from the menu, expand the Train selection and select Always.
Note: If the tool is able to read the full image database of all of the characters in your images with 100% accuracy, you do not need to train.