使用混合模型和文档特定微调的中世纪手稿上的开源手写文本识别
Open Source Handwritten Text Recognition on Medieval Manuscripts using Mixed Models and Document-Specific Finetuning
摘要：
本文论述了实用和开源的手写文本识别（HTR）的任务，对德国中世纪手稿。我们报告了我们构建混合识别模型的努力，这些模型可以在没有任何进一步的特定于文档的训练的情况下开箱即用，但也可以作为微调的起点，通过在几页转录文本（地面实况）上训练一个新模型。为了对混合模型进行训练，我们收集了35篇论文和案例。12.5k文本行，用于两种广泛使用的手写风格，哥特式和Bastarda草书。在四个未见过的手稿上评估混合模型，平均字符出错率（CER）为6.22%。经过2页、4页和最终32页的训练后，CER分别下降到3.27%、2.58%和1.65%。虽然模型的域内识别和训练（Bastarda模型到Bastarda材料，Gothic到Gothic）毫无疑问产生了最好的结果，但将领域模型微调到看不见的脚本仍然优于从头开始训练。我们的新混合模型已公开提供给社区。
英文摘要：
This paper deals with the task of practical and open source Handwritten Text Recognition (HTR) on German medieval manuscripts. We report on our efforts to construct mixed recognition models which can be applied out-of-the-box without any further document-specific training but also serve as a starting point for finetuning by training a new model on a few pages of transcribed text (ground truth). To train the mixed models we collected a corpus of 35 manuscripts and ca. 12.5k text lines for two widely used handwriting styles, Gothic and Bastarda cursives. Evaluating the mixed models out-of-the-box on four unseen manuscripts resulted in an average Character Error Rate (CER) of 6.22%. After training on 2, 4 and eventually 32 pages the CER dropped to 3.27%, 2.58%, and 1.65%, respectively. While the in-domain recognition and training of models (Bastarda model to Bastarda material, Gothic to Gothic) unsurprisingly yielded the best results, finetuning out-of-domain models to unseen scripts was still shown to be superior to training from scratch. Our new mixed models have been made openly available to the community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
