# Can I generate adjacency matrices from directed graph images? [closed]

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?

## closed as off-topic by Sycorax, Michael Chernick, mdewey, Peter Flom♦Jun 18 '18 at 11:03

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• Ok after further research about the problem I found out that scikit-image might be able to help solve this problem. I am particularily looking at hough transformations. But still I couldn't find any ready-to-use implementations, even though this problem appears rather basic to me. Does anyone have an idea on how to implement a directed graph to adjacency matrix conversion algorithm with scikit-image? – Julian Pr Jan 11 '17 at 16:05
• Go for openCV and focus on computer-images (not hand drawn) and undirected graphs. If you are able to solve this much more simple problem, then you might continue. Further, define expectations regarding circle size and line thickness. Finally you will still need to extract the letter inside the circle. – Nikolas Rieble Jan 30 '17 at 9:57