Classifying Letters using CNN - Help
So there are some things you could consider.

First of all, normally datasets like this (or for example mnist) will be one hot encoded. So you have a vector with the length of all classes, each position stands for one class and is 1 if it is the class and 0 otherwise.
As you are predicting values between zero and one, the activation of your output should do the same. So the first thing i would change is the activation function of the last layer to sigmoid. It is linear in your case.

Other things to consider are data preprocessing like normalizing pixel values between 0 and 1. Also no data preprocessing at all can lead to unstable training it is unlikely to cause no learning at all.