I would be glad if someone could give me some hints and assessment for the following project. (I'm relatively new to ML and DL and having only a little theoretical knowledge)
My goal is to build a detector for receipt corners in images. I started to create a dataset with images of the receipts with the labels being the 4 corner points of the receipt.
My plan is to train a CNN with the dataset and I wonder if you could give me an estimation on how much images I would need in my dataset to successfully train it (will it be a few hundred or several thousand)? Would this be a quite simple task for the network or either complex due to the large amount fo pixels in the images?
Edit: (Thanks for your answers so far!)
- My data is an image with a list the corner points of the receipt [[x, y], [x, y], [x, y], [x, y]]
- I'm planning to use a NN to output me these 4 corner points
- In the next step the background shall be cropped using these 4 points
I started using a pre-trained ResNet18 using pytorch and got stuck with the following questions, as the task differs from the basic classification tutorials I found so far:
- How do I need to transform the label vector with the 4 corners?
- How does the output look like?
- Do I need to use a FCN for this task as its a kind of segmentation task?