  Historical manuscript alignment is a widely known problem in document
analysis. Finding the differences between manuscript editions is mostly done
manually. In this paper, we present a writer independent deep learning model
which is trained on several writing styles, and able to achieve high detection
accuracy when tested on writing styles not present in training data. We test
our model using cross validation, each time we train the model on five
manuscripts, and test it on the other two manuscripts, never seen in the
training data. We've applied cross validation on seven manuscripts, netting 21
different tests, achieving average accuracy of $\%92.17$. We also present a new
alignment algorithm based on dynamic sized sliding window, which is able to
successfully handle complex cases.
