  The ability of a document classifier to handle inputs that are drawn from a
distribution different from the training distribution is crucial for robust
deployment and generalizability. The RVL-CDIP corpus is the de facto standard
benchmark for document classification, yet to our knowledge all studies that
use this corpus do not include evaluation on out-of-distribution documents. In
this paper, we curate and release a new out-of-distribution benchmark for
evaluating out-of-distribution performance for document classifiers. Our new
out-of-distribution benchmark consists of two types of documents: those that
are not part of any of the 16 in-domain RVL-CDIP categories (RVL-CDIP-O), and
those that are one of the 16 in-domain categories yet are drawn from a
distribution different from that of the original RVL-CDIP dataset (RVL-CDIP-N).
While prior work on document classification for in-domain RVL-CDIP documents
reports high accuracy scores, we find that these models exhibit accuracy drops
of between roughly 15-30% on our new out-of-domain RVL-CDIP-N benchmark, and
further struggle to distinguish between in-domain RVL-CDIP-N and out-of-domain
RVL-CDIP-O inputs. Our new benchmark provides researchers with a valuable new
resource for analyzing out-of-distribution performance on document classifiers.
Our new out-of-distribution data can be found at
https://github.com/gxlarson/rvl-cdip-ood.
