  From small screenshots to large videos, documents take up a bulk of space in
a modern smartphone. Documents in a phone can accumulate from various sources,
and with the high storage capacity of mobiles, hundreds of documents are
accumulated in a short period. However, searching or managing documents remains
an onerous task, since most search methods depend on meta-information or only
text in a document. In this paper, we showcase that a single modality is
insufficient for classification and present a novel pipeline to classify
documents on-device, thus preventing any private user data transfer to server.
For this task, we integrate an open-source library for Optical Character
Recognition (OCR) and our novel model architecture in the pipeline. We optimise
the model for size, a necessary metric for on-device inference. We benchmark
our classification model with a standard multimodal dataset FOOD-101 and
showcase competitive results with the previous State of the Art with 30% model
compression.
