Digital note-taking is gaining popularity, offering a durable, editable, and
easily indexable way of storing notes in the vectorized form, known as digital
ink. However, a substantial gap remains between this way of note-taking and
traditional pen-and-paper note-taking, a practice still favored by a vast
majority. Our work, InkSight, aims to bridge the gap by empowering physical
note-takers to effortlessly convert their work (offline handwriting) to digital
ink (online handwriting), a process we refer to as Derendering. Prior research
on the topic has focused on the geometric properties of images, resulting in
limited generalization beyond their training domains. Our approach combines
reading and writing priors, allowing training a model in the absence of large
amounts of paired samples, which are difficult to obtain. To our knowledge,
this is the first work that effectively derenders handwritten text in arbitrary
photos with diverse visual characteristics and backgrounds. Furthermore, it
generalizes beyond its training domain into simple sketches. Our human
evaluation reveals that 87% of the samples produced by our model on the
challenging HierText dataset are considered as a valid tracing of the input
image and 67% look like a pen trajectory traced by a human.

数字记事正在流行，我们的研究 InkSight 致力于通过转换手写笔记到数字墨迹来弥合数字记事和传统纸笔记的差距，这是第一个有效地将手写文本转换为各种具有不同视觉特征和背景的照片的工作。

InkSight: 学习阅读与书写的离线到在线手写转换

InkSight: Offline-to-Online Handwriting Conversion by Learning to Read  and Write

Script identification and text recognition are some of the major domains in
the application of Artificial Intelligence. In this era of digitalization, the
use of digital note-taking has become a common practice. Still, conventional
methods of using pen and paper is a prominent way of writing. This leads to the
classification of scripts based on the method they are obtained. A survey on
the current methodologies and state-of-art methods used for processing and
identification would prove beneficial for researchers. The aim of this article
is to discuss the advancement in the techniques for script pre-processing and
text recognition. In India there are twelve prominent Indic scripts, unlike the
English language, these scripts have layers of characteristics. Complex
characteristics such as similarity in text shape make them difficult to
recognize and analyze, thus this requires advance preprocessing methods for
their accurate recognition. A sincere attempt is made in this survey to provide
a comparison between all algorithms. We hope that this survey would provide
insight to a researcher working not only on Indic scripts but also other
languages.

该研究论文调查了脚本识别和文本识别的现有方法和技术，并讨论了在处理和识别脚本方面的技术改进，特别是针对印度的 12 种主要印度文字的复杂特征和高级预处理方法。