If a neural network you have trained is consistently failing to recognize a specific character or characters, try the following.
Check that you thinned the example characters before adding them to the training set
If you have forgotten to thin the example characters before adding them to the training set the neural network is unlikely to be able to learn to recognize them. If this has happened you will have to create the training set again.
Continue to train the neural network
Because of the random nature of font training the neural network may, by chance, learn to recognize some characters better than others. This is particularly true if you have a large training set. Continuing training gives the network another chance to learn to recognize characters that it recognizes poorly.
To continue training a neural network, select Train Menu > Train Neural Net. Type a Target Percentage larger than the one you used when you trained the network the first time. For example, if you trained the network to 95% accuracy initially, try 96 or 97%.
Check that the character is correctly assigned in the training set
If you have added an example letter A to the training set but have accidentally told Scan2CAD that it is a letter B, the neural network will have problems recognizing one or both of these characters. Check that the character is correctly assigned.
Check that the incorrectly recognized character is adequately represented in the training set
If you have included ten examples of the letter A in the training set but only one example of the letter B, the random nature of font training means that the neural network is less likely to learn to recognize the letter B than the letter A.
To check whether a character is adequately represented in the training set:
How many examples of the incorrectly recognized character are there in the training set compared to other characters?
Add more examples of the character to the training set
The neural network may fail to recognize a character because it is too different from the examples of that character in the training set – in other words the character displays more variation than the network has been trained to expect. In this case add the characters that the neural network has failed to recognize to the training set as examples, as follows:
Troubleshooting neural networks
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