论文标题
全页识别历史手写
Whole page recognition of historical handwriting
论文作者
论文摘要
历史手写文件仅在少数学者和专家范围内保护人类知识的重要组成部分。机器学习和手写研究的最新发展具有使这些信息可访问和搜索的潜力。为此，我们研究了一种无文本本地化的端到端推理方法，该方法涉及手写页面并抄录其全文。推理中不涉及明确的字符，单词或线条细分，这就是为什么我们将这种方法称为“无分段”的原因。与基于IAM，Rodrigo和Scribblels Corpora的逐行分段方法相比，我们探索了它的稳健性和准确性，该方法的三种语言具有400年的手写样式。我们专注于可以部署在手持或嵌入式设备上的模型类型和尺寸。我们得出的结论是，没有文本本地化和细分的整个页面推理方法具有竞争力。
Historical handwritten documents guard an important part of human knowledge only within reach of a few scholars and experts. Recent developments in machine learning and handwriting research have the potential of rendering this information accessible and searchable to a larger audience. To this end, we investigate an end-to-end inference approach without text localization which takes a handwritten page and transcribes its full text. No explicit character, word or line segmentation is involved in inference which is why we call this approach "segmentation free". We explore its robustness and accuracy compared to a line-by-line segmented approach based on the IAM, RODRIGO and ScribbleLens corpora, in three languages with handwriting styles spanning 400 years. We concentrate on model types and sizes which can be deployed on a hand-held or embedded device. We conclude that a whole page inference approach without text localization and segmentation is competitive.
