model for hand written text recognition
I see two options here:

You can construct your model to recognize separate letters of those words, then there are as many nodes in the last layer as there are letters and symbols in the alphabet that your model will read.
You can make output of your model as a vector and then "decode" this vector using some other tool that can encode/decode words as vectors. One such tool I can think of is word2vec. Or there's an option to download some database of possible words and create such a tool yourself.
Description of your model is very vague. If you want to get more specific help, then you should provide more info, e.g. some model architecture.
I have been attempting to create a model that given an image, can read the text from it. I am attempting to do this by implementing a cnn, rnn, and ctc. I am doing this with TensorFlow and Keras. There are a couple of things I am confused about. For reading single digits, I understand that your last layer in the model should have 9 nodes, since those are the options. However, for reading words, aren't there infinitely many options, so how many nodes should I have in my last layer. Also, I am confused as to how I should add my ctc to my Keras model. Is it as a loss function?