Publication Details
Brno Mobile OCR Dataset
OCR, CTC, mobile, dataset
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 %, 23 % 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.
@inproceedings{BUT162131,
author="Martin {Kišš} and Michal {Hradiš} and Oldřich {Kodym}",
title="Brno Mobile OCR Dataset",
booktitle="Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",
year="2020",
pages="1352--1357",
publisher="Institute of Electrical and Electronics Engineers",
address="Sydney",
doi="10.1109/ICDAR.2019.00218",
isbn="978-1-7281-3015-6",
url="https://pero.fit.vutbr.cz/publications"
}