  Reading order detection is the cornerstone to understanding visually-rich
documents (e.g., receipts and forms). Unfortunately, no existing work took
advantage of advanced deep learning models because it is too laborious to
annotate a large enough dataset. We observe that the reading order of WORD
documents is embedded in their XML metadata; meanwhile, it is easy to convert
WORD documents to PDFs or images. Therefore, in an automated manner, we
construct ReadingBank, a benchmark dataset that contains reading order, text,
and layout information for 500,000 document images covering a wide spectrum of
document types. This first-ever large-scale dataset unleashes the power of deep
neural networks for reading order detection. Specifically, our proposed
LayoutReader captures the text and layout information for reading order
prediction using the seq2seq model. It performs almost perfectly in reading
order detection and significantly improves both open-source and commercial OCR
engines in ordering text lines in their results in our experiments. We will
release the dataset and model at \url{https://aka.ms/layoutreader}.
