How to recognise arrows on image?

1

Hello. I am trying recognise arrow on the image. I made the skeletonization and then i used erosion to detect statistic functions. I used kernel:
([ 0, 0, 0], [1, 1, 1], [0, 0, 0]) to erode skeletonized image. Thanks to that i have knowledge that it is horizontal arrow - left or right. I did the same for vertical lines.
Now I got stuck since few days and I cannot make any progress how to detect which exactly arrow it is. I was trying hit-or-miss method but it isn’t working for all cases.
I would be glad to hear some of your proposals.

I attached few screenshots from my program.

arrow1
arrow11068×815 20.4 KB

2

I guess you could instead find contours and then calculate the portion of contour’s points in, for example, in the left third of the width of a horizontal arrow - as the pointing end has more points than other, and the other type has the edges nearer the center than the other.

3

humans call “arrow” a variety of different shapes. an arrow can vary in the thickness and length of the shaft, the angle of the tip, do the sides of the tip curve and how much, is the back of the tip straight or curved or nonexistent (sides are just lines, tip isn’t solid)…

if you have a limited number of shapes, and you only have scaled (and translated) instances of them, I see a few options:

  • you could get away with a cascade classifier/detector. that’s very old technology but fairly understood

  • you could also perform shape matching. that uses contours

if you don’t have a limited number of shapes, it is unlikely that such low-level methods alone can solve the problem. I also think that skeletonization won’t lead to any useful information.

edge information seems useful.

an arrow typically has a long shaft, a pointy tip, and they’re arranged to each other just so.

at the very least you’ll need a “parts model”. that already enters the realm of machine learning.

you might even need deep learning (maybe not). it’s a possible solution with different tradeoffs.