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

• URL: http://arxiv.org/abs/2212.02135v3
• Date: Tue, 19 Sep 2023 13:21:49 GMT
• Title: SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-Labels
• Authors: Martin Ki\v{s}\v{s}, Michal Hradi\v{s}, Karel Bene\v{s}, Petr Buchal, Michal Kula
• Abstract summary: This paper explores semi-supervised sequence, such as Optical Character Recognition or Automatic Automatic Recognition. We propose a novel loss filtering function $xx2013$ SoftCTC.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 na\"ive CTC-based approach for training on multiple transcription variants, and we make our GPU implementation public.

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