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arXiv · 2212.02135

SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-Labels

Martin Kišš·Michal Hradiš·Karel Beneš·Petr Buchal·Michal Kula
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Abstract

This paper explores semi-supervised training for sequence tasks, such as Optical Character Recognition or Automatic Speech Recognition. We propose a novel loss function $\unicode{x2013}$ SoftCTC $\unicode{x2013}$ which is an extension of CTC allowing to consider multiple transcription variants at the same time. This allows to omit the confidence based filtering step which is otherwise a crucial component of pseudo-labeling approaches to semi-supervised learning. We demonstrate the effectiveness of our method on a challenging handwriting recognition task and conclude that SoftCTC matches the performance of a finely-tuned filtering based pipeline. We also evaluated SoftCTC in terms of computational efficiency, concluding that it is significantly more efficient than a naïve CTC-based approach for training on multiple transcription variants, and we make our GPU implementation public.

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Martin Kišš, Michal Hradiš, Karel Beneš, Petr Buchal, Michal Kula. 2023-09-19. SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-Labels. https://arxiv.org/abs/2212.02135

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