  The task of writer verification is to provide a likelihood score for whether
the queried and known handwritten image samples belong to the same writer or
not. Such a task calls for the neural network to make it's outcome
interpretable, i.e. provide a view into the network's decision making process.
We implement and integrate cross-attention and soft-attention mechanisms to
capture the highly correlated and salient points in feature space of 2D inputs.
The attention maps serve as an explanation premise for the network's output
likelihood score. The attention mechanism also allows the network to focus more
on relevant areas of the input, thus improving the classification performance.
Our proposed approach achieves a precision of 86\% for detecting intra-writer
cases in CEDAR cursive "AND" dataset. Furthermore, we generate meaningful
explanations for the provided decision by extracting attention maps from
multiple levels of the network.
