incorrect image classification using NN
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Sarah Mahmood
on 21 Sep 2013
Commented: Sarah Mahmood
on 30 Sep 2013
hi all i have implemented a neural network to recognize printed character (0-9 and L R ), I got correct classification when testing in offline mode i.e. on previously captured images but when i connect the camera and test the NN in the online mode i got completely incorrect classification i used wavelets as features extractor with resolution 4 and i noticed some differences in features extracted during online capturing and the straining one , can anyone help me out figure the error or advice me with something
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Image Analyst
on 26 Sep 2013
Well, there aren't that many NN experts around. Perhaps you'd be willing to use more traditional methods, like here. Anyway, OCR questions never get a lot of help in this forum - they're just too complicated and involved for us to answer. Most people want a turnkey OCR program just handed over to them, and we just can't do that. We can help on small snippets of code only.
Accepted Answer
Greg Heath
on 29 Sep 2013
Edited: Greg Heath
on 29 Sep 2013
Test the program offline on non-training images before testing it online.
I suspect you have over-trained an over-fit net so that it essentially memorized the training images but is not able to generalize to non-training images.
How many input/target examples do you have? N = ?
What is the dimensionality of your input feature vectors? I = ?
[ I N ] = size(input)
What is the dimensionality of your output/target classification vectors? O = ?
[ O N ] = size(target)
Since you have c = 12 categories, O = c = 12 with target matrix columns equal to c-dimensional unit vectors with the row index of the 1 indicating the true class index of the corresponding input vector.
What are the sizes of the train/val/test sets? Ntrn/Nval/Ntst = ?
How many hidden-layer nodes? H = ?
What is the ratio of training equations Ntrneq = Ntrn*O to unknown weights Nw = (I+1)*H+(H+1)*O ?
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