Purpose: In this paper, we establish a baseline for handwritten stenography
recognition, using the novel LION dataset, and investigate the impact of
including selected aspects of stenographic theory into the recognition process.
We make the LION dataset publicly available with the aim of encouraging future
research in handwritten stenography recognition.
Methods: A state-of-the-art text recognition model is trained to establish a
baseline. Stenographic domain knowledge is integrated by applying four
different encoding methods that transform the target sequence into
representations, which approximate selected aspects of the writing system.
Results are further improved by integrating a pre-training scheme, based on
synthetic data.
Results: The baseline model achieves an average test character error rate
(CER) of 29.81% and a word error rate (WER) of 55.14%. Test error rates are
reduced significantly by combining stenography-specific target sequence
encodings with pre-training and fine-tuning, yielding CERs in the range of
24.5% - 26% and WERs of 44.8% - 48.2%.
Conclusion: The obtained results demonstrate the challenging nature of
stenography recognition. Integrating stenography-specific knowledge, in
conjunction with pre-training and fine-tuning on synthetic data, yields
considerable improvements. Together with our precursor study on the subject,
this is the first work to apply modern handwritten text recognition to
stenography. The dataset and our code are publicly available via Zenodo.

通过使用新颖的 LION 数据集建立基线，并研究将选定的速记理论纳入识别过程的影响，本研究在手写速记识别领域奠定基础，并通过集成速记领域知识、预训练和微调技术，显著提高了测试误差率。