Brno Mobile OCR Dataset

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU135406" target="_blank" >RIV/00216305:26230/20:PU135406 - isvavai.cz</a>

Result on the web

<a href="https://pero.fit.vutbr.cz/publications" target="_blank" >https://pero.fit.vutbr.cz/publications</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDAR.2019.00218" target="_blank" >10.1109/ICDAR.2019.00218</a>

Result language

angličtina

Original language name

Brno Mobile OCR Dataset

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/DG18P02OVV055" target="_blank" >DG18P02OVV055: Advanced content extraction and recognition for printed and handwritten documents for better accessibility and usability</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

ISBN

978-1-7281-3014-9

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

1352-1357

Publisher name

Institute of Electrical and Electronics Engineers

Place of publication

Sydney

Event location

Sydney, Australia

Event date

Sep 20, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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