I have some images which look like this one:
They exist of 3 possible characters (A-C) and a length of 4.
Now, I would like to run a neural network, which recognizes each character in the picture without any segmentation before that; I would like to use a similar approach as the authors in this paper did: https://medusa.fit.vutbr.cz/traffic/research-topics/general-traffic-analysis/holistic-recognition-of-low-quality-license-plates-by-cnn-using-track-annotated-data-iwt4s-avss-2017/.
Now, I am wondering which kind of classification problem I am facing and how to solve that?
Is this a multi-label classification problem? Or a multi-class problem? Or is it a sequential data labeling problem (I assume this)? Or something completely different?
To differentiate the different problems: Multi-label classification would mean: First class is 'AAAA', second class 'AAAB', ... and the last class 'CCCC'. That means, we would have 3x3x3x3 = 81 possible classes. So, we would have one column for each label which contains a string label.
Multi-label classification would mean: The labels are split in parts for every character. First position: A-D. Second: A-D, Third: A-D, Fourth: A-D. So, we would have 4 columns for each label with the appropriate character.
I think, both are not the approach the authors used. If I understood correctly the paper I linked above, they handled the data as sequential data, and I do not have to split the labels but I can use the whole label. Futhermore, the convolutional neural network is able to learn the spatial positions automatically. Because of that, I do not have to split the labels but can them use as they are. If this is right, then I think the target shape of my output should be something like (n, 4, 3), with n as the number of images, 4 as the length of the text labels and 3 as the possibilities for each character.
Is this correct?
