Abstract
With the advent of deep neural networks, handwriting recognition systems have recently achieved remarkable performance. Unfortunately, to achieve high-quality results, these models require large amounts of labeled training data, which is difficult to obtain. Various methods have been proposed to reduce the volume of training data required. We propose a framework for fitting new handwriting recognition models that joins both active and transfer learning into a unified framework. Empirical results show that our method performs better than traditional active learning, transfer learning, and standard supervised training methods.
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Burdett, E. et al. (2022). Active Transfer Learning for Handwriting Recognition. In: Porwal, U., Fornés, A., Shafait, F. (eds) Frontiers in Handwriting Recognition. ICFHR 2022. Lecture Notes in Computer Science, vol 13639. Springer, Cham. https://doi.org/10.1007/978-3-031-21648-0_17
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