  Text reading order is a crucial aspect in the output of an OCR engine, with a
large impact on downstream tasks. Its difficulty lies in the large variation of
domain specific layout structures, and is further exacerbated by real-world
image degradations such as perspective distortions. We propose a lightweight,
scalable and generalizable approach to identify text reading order with a
multi-modal, multi-task graph convolutional network (GCN) running on a sparse
layout based graph. Predictions from the model provide hints of bidimensional
relations among text lines and layout region structures, upon which a
post-processing cluster-and-sort algorithm generates an ordered sequence of all
the text lines. The model is language-agnostic and runs effectively across
multi-language datasets that contain various types of images taken in
uncontrolled conditions, and it is small enough to be deployed on virtually any
platform including mobile devices.
