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Impact of Training LSTM-RNN with Fuzzy Ground Truth

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

Proceedings of ICPRAM International Conference on Pattern Recognition Applications and Methods (ICPRAM-18), 7th International Conference on Pattern Recognition Applications and Methods, January 16-18, Funchal, Madeira, Portugal , SCITEPRESS Digital Library , 2018
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 When annotating historical documents or low quality scans deciding on the correct annotation is difficult especially for non-experts.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.

Show BibTex:

@inproceedings {
       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 When annotating historical documents or low quality scans deciding on the correct annotation is difficult especially for non-experts.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.},
       number = {}, 
       month = {}, 
       year = {2018}, 
       title = {Impact of Training LSTM-RNN with Fuzzy Ground Truth}, 
       journal = {}, 
       volume = {}, 
       pages = {}, 
       publisher = {SCITEPRESS Digital Library}, 
       author = {Martin Jenckel, Sourabh Sarvotham Parkala, Syed Saqib Bukhari, Andreas Dengel}, 
       keywords = {Document Analysis, OCR, LSTM, Fuzzy Ground Truth},
       url = {}
}