Impact of Training LSTM-RNN with Fuzzy Ground Truth

Martin Jenckel, Sourabh Sarvotham Parkala, Syed Saqib Bukhari, Andreas Dengel

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 When annotating historical documents or low quality scans deciding on the correct annotation is difficult especially for non-experts.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 ground truth achieves a similar performance.

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Paper Citation


in Harvard Style

Jenckel M., Parkala S., Bukhari 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 - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 388-393. DOI: 10.5220/0006592703880393


in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2018},
pages={388-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006592703880393},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Impact of Training LSTM-RNN with Fuzzy Ground Truth
SN - 978-989-758-276-9
AU - Jenckel M.
AU - Parkala S.
AU - Bukhari S.
AU - Dengel A.
PY - 2018
SP - 388
EP - 393
DO - 10.5220/0006592703880393