![](bg7.png)
d04-012-10. Finally, an example of deletion is present
in the case of sample csg562-042-24. As already
mentioned, errors are present in the datasets. In the
IAM dataset, the GT transcriptions have many more
spaces than the correct ones, especially before punc-
tuation marks. Moreover, errors in the transcription
are present, such as in the case of d04-012-10 where
the GT transcription contains the final dot not present
in the image itself.
5 CONCLUSIONS
We empirically demonstrated that relaxation label-
ing processes help in generalisation abilities for
well-established architectures in the HTR field, the
CRNNs. Such processes can drive the network to-
wards a more consistent labeling output in all the
datasets considered. They improve the results in
terms of both validation and test CER and WER. As
a future work we consider to compare the relaxation
labeling module with attention mechanisms, which
have a similar role of contextual processing from the
context. Finally, we plan to conduct a more exten-
sive comparison of our proposed method with other
backbones, to consistently evaluate the performance
improvement provided by relaxation labeling.
ACKNOWLEDGEMENTS
We would especially like to thank Gregory Sech for
his valuable suggestions and his past work on Relax-
ation Labeling processes. Also, we want to thank the
Italian Institute of Technology (IIT) for the possibil-
ity to use their High-Performance Computing (HPC)
as it allowed for faster experimentation.
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