I have some images of very simple directed graphs with just a few nodes and edges. I know there is lots of tools, which print out graphs with a adjacency matrix is an input, but I need to do it the other way around.
For example Das and Chanda's paper covers this technique, but on a more advanced level. Their approach covers quite complex graphs, possibly even hand-written.
However the graphs I am targeting are much simpler and all machine generated. For example if I get this image as an input:
I would expect the following adjacency matrix as an output:
+---+---+---+ | | A | B | +---+---+---+ | A | 0 | 1 | | B |-1 | 0 | +---+---+---+
Does someone know a tool that is capable doing this or can provide me some resources on how I can develop such a tool myself?
I was thinking about a convolutional neural network for simple object recognition, which in this case would be the nodes and edges. Do you think this approach might work or would it be overkill?