You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This issue I find a showstopper for me actually.
all Ws even the ones that are clear and straight are recognized as N. I guess this is something to do with the training.
I don't expect a solution here (but feel free to surprise me :)) but just to let you know that you might be expecting that.
The text was updated successfully, but these errors were encountered:
Hello @kubasiak,
The Mobilenet model which I used to train for character recognition is a lightweight architecture, which leads to the fact it might be underfitting in some scenarios. The case between W and N is a good example since in the context of computer vision, letter W is just an extend of letter N.
The solution might be to use a deeper NN such as ResNet50 or add more dataset in the categories of W and N. In the commercial product which I am working right now, we developed a YOLO detector which can detect and recognize license characters directly from plate image. This technique showed a much better accuracy due to the fact that we have really good dataset and no manual segmenting step is required.
This issue I find a showstopper for me actually.
all Ws even the ones that are clear and straight are recognized as N. I guess this is something to do with the training.
I don't expect a solution here (but feel free to surprise me :)) but just to let you know that you might be expecting that.
The text was updated successfully, but these errors were encountered: