Handwritting Recognition moving from character level to word level Given the experience on MIST, I try this problem as a character level.  

I have a handwritten text and I want to "OCR" it. Even though I made progresses with openCV (on the image pre-processing, before a DNN classifier).

I think that the best approach would be to move to word level (into RNN). I am writing this in Python. But I don't know how to change the last layer of the DNN into an input layer for RNN.

Is there any code for dummies to see how is this done (for images as input of the first DNN layer)? Besides I see that you could put a language model on top (this would be a dictionary right?). Advice?
I do not think that you would need a recurrent neural net here. This will be much slower to train than a ConvNet. Additionally, your data is images, correct? I think you could use a deep convolutional net to a fully-connected net. This should be fast and perform well. Please look at the following archived blogs from keras.
Building powerful image classification models using very little data
How convolutional neural networks see the world