CODEWITHSUNDEEP
neural-networkocr
Januszoff
Januszoff

Reputation: 315

Which method should theoretically be best?

Say I want to recognize my characters using neural network(s). Let's cut it down to 5 letters, the binary form of image to 16x16, input + 2 layers network, unipolar function inside both layers. Momentum backpropagation is used in the process of learning.

Which of the following approaches should give the best results (where x is the number of neurons in the first layer)? By best I mean highest % of correct recognitions. Speed isn't a factor in this question.

  1. A single network, 256;x;5 - the highest value neuron wins.
  2. 5 networks, 256;x;1 - each network has it's own letter, each output is tested vs a treshold, could happen that 2 or more networks recognize an image as "their own"
  3. Same as above, but now the output from each network is normalized (if a,b,c,d,e are outputs, then a = a / (a+b+c+d+e); b = b / (a+b+c+d+e) and so on)

Upvotes: 0

Views: 49

Answers (1)

Denis Tarasov
Denis Tarasov

Reputation: 1051

2 is the worst option because "2 or more networks recognize an image as "their own"" will definitly happen many times and how you descriminate between them after that? 1 will work reasonable well. 3 is the basic idea behind softmax output function and softmax usually works best for classification tasks especially when combined with cross-entropy error function.

Upvotes: 1

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