  We present CloudScan; an invoice analysis system that requires zero
configuration or upfront annotation. In contrast to previous work, CloudScan
does not rely on templates of invoice layout, instead it learns a single global
model of invoices that naturally generalizes to unseen invoice layouts. The
model is trained using data automatically extracted from end-user provided
feedback. This automatic training data extraction removes the requirement for
users to annotate the data precisely. We describe a recurrent neural network
model that can capture long range context and compare it to a baseline logistic
regression model corresponding to the current CloudScan production system. We
train and evaluate the system on 8 important fields using a dataset of 326,471
invoices. The recurrent neural network and baseline model achieve 0.891 and
0.887 average F1 scores respectively on seen invoice layouts. For the harder
task of unseen invoice layouts, the recurrent neural network model outperforms
the baseline with 0.840 average F1 compared to 0.788.
