loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Martin Jenckel 1 ; Sourabh Sarvotham Parkala 2 ; Syed Saqib Bukhari 3 and Andreas Dengel 1

Affiliations: 1 German Research Center for Artificial Intelligence (DFKI) and TU Kaiserslautern, Germany ; 2 TU Kaiserslautern, Germany ; 3 German Research Center for Artificial Intelligence (DFKI), Germany

Keyword(s): Document Analysis, OCR, LSTM, Fuzzy Ground Truth.

Abstract: Most machine learning algorithms follow the supervised learning approach and therefore require annotated training data. The large amount of training data required to train state of the art deep neural networks changed the methods of acquiring the required annotations. User annotations or completely synthetic annotations are becoming more and more prevalent replacing careful manual annotations by experts. In the field of OCR recent work has shown that synthetic ground truth acquired through clustering with minimal manual annotation yields good results when combined with bidirectional LSTM-RNN. Similarly we propose a change to standard LSTM training to handle imperfect manual annotation. When annotating historical documents or low quality scans deciding on the correct annotation is difficult especially for non-experts. Providing all possible annotations in such cases, instead of just one, is what we call fuzzy ground truth. Finally we show that training an LSTM-RNN on fuzzy gr ound truth achieves a similar performance. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.118.139.79

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Jenckel, M., Parkala, S. S., Bukhari, S. S. and Dengel, A. (2018). Impact of Training LSTM-RNN with Fuzzy Ground Truth. In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-276-9; ISSN 2184-4313, SciTePress, pages 388-393. DOI: 10.5220/0006592703880393

@conference{icpram18,
author={Martin Jenckel and Sourabh Sarvotham Parkala and Syed Saqib Bukhari and Andreas Dengel},
title={Impact of Training LSTM-RNN with Fuzzy Ground Truth},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2018},
pages={388-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006592703880393},
isbn={978-989-758-276-9},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Impact of Training LSTM-RNN with Fuzzy Ground Truth
SN - 978-989-758-276-9
IS - 2184-4313
AU - Jenckel, M.
AU - Parkala, S.
AU - Bukhari, S.
AU - Dengel, A.
PY - 2018
SP - 388
EP - 393
DO - 10.5220/0006592703880393
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic