OCR-IDL: OCR Annotations for Industry Document Library Dataset

Ali Furkan Biten*, Rubèn Tito, Lluis Gomez, Ernest Valveny, Dimosthenis Karatzas

*Corresponding author for this work

Research output: Chapter in BookChapterResearchpeer-review


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. 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.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022 Workshops, Proceedings
EditorsLeonid Karlinsky, Tomer Michaeli, Ko Nishino
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031250682
Publication statusPublished - 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13804 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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