  Pretraining has proven successful in Document Intelligence tasks where deluge
of documents are used to pretrain the models only later to be finetuned on
downstream tasks. One of the problems of the pretraining approaches is the
inconsistent usage of pretraining data with different OCR engines leading to
incomparable results between models. In other words, it is not obvious whether
the performance gain is coming from diverse usage of amount of data and
distinct OCR engines or from the proposed models. To remedy the problem, we
make public the OCR annotations for IDL documents using commercial OCR engine
given their superior performance over open source OCR models. The contributed
dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope
that OCR-IDL can be a starting point for future works on Document Intelligence.
All of our data and its collection process with the annotations can be found in
https://github.com/furkanbiten/idl_data.
