  We introduce the Brno Mobile OCR Dataset (B-MOD) for document Optical
Character Recognition from low-quality images captured by handheld mobile
devices. While OCR of high-quality scanned documents is a mature field where
many commercial tools are available, and large datasets of text in the wild
exist, no existing datasets can be used to develop and test document OCR
methods robust to non-uniform lighting, image blur, strong noise, built-in
denoising, sharpening, compression and other artifacts present in many
photographs from mobile devices.
  This dataset contains 2 113 unique pages from random scientific papers, which
were photographed by multiple people using 23 different mobile devices. The
resulting 19 728 photographs of various visual quality are accompanied by
precise positions and text annotations of 500k text lines. We further provide
an evaluation methodology, including an evaluation server and a testset with
non-public annotations.
  We provide a state-of-the-art text recognition baseline build on
convolutional and recurrent neural networks trained with Connectionist Temporal
Classification loss. This baseline achieves 2 %, 22 % and 73 % word error rates
on easy, medium and hard parts of the dataset, respectively, confirming that
the dataset is challenging.
  The presented dataset will enable future development and evaluation of
document analysis for low-quality images. It is primarily intended for
line-level text recognition, and can be further used for line localization,
layout analysis, image restoration and text binarization.
