@conference {40310, title = {ICDAR2017 Competition on Post-OCR Text Correction}, booktitle = {2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)}, year = {2018}, month = {2017/11}, publisher = {ICDAR}, organization = {ICDAR}, address = {Kyoto, Japon}, abstract = {

This paper describes the ICDAR2017 competition
on post-OCR text correction and presents the different methods
submitted by the participants. OCR has been an active research
field for over the past 30 years but results are still imperfect,
especially for historical documents. The purpose of this competition
is to compare and evaluate automatic approaches for
correcting (denoising) OCR-ed texts. The challenge consists of
two independent tasks: 1) error detection and 2) error correction.
An original dataset of 12M OCR-ed symbols along with an
aligned ground truth was provided to the participants with
80\% of the dataset dedicated to the training and 20\% to the
evaluation. Different sources were aggregated and namely contain
newspapers and monographs covering 2 languages (English and
French). 11 teams submitted results, while the difficulty of the
task was underlined by the fact that only half of the submitted
methods were able to denoise the evaluation dataset on average.
In any case, this competition, which counted 35 registrations,
illustrates the strong interest of the community in this essential
problem, which is key to any digitization process involving textual
data.

}, keywords = {OCR, OCR errors}, author = {Jean-Philippe Moreux and Guillaume Chiron and Antoine Doucet and Mikael Coustady} }