Currently I'm learning about neural networks and I'm trying to create an application that can be trained to recognize handwritten characters. For this problem I use a feed-forward neural network and it seems to work when I train it to recognize 1, 2 or 3 different characters. But when I try to make the network learn more than 3 characters it will stagnate at a error percentage around the 40 - 60%.
I tried with multiple layers and less/more neurons but I can't seem to get it right, now I'm wondering if a feedforward neural network is capable of recognizing that much information.
Some statistics:
Network type: Feed-forward neural network
Input neurons: 100 (a 10 * 10) grid is used to draw the characters
Output neurons: The amount of characters to regocnize
Does anyone know what's the possible flaw in my architecture is? Are there too much input neurons? Is the feedforward neural network not capable of character regocnition?
