With the surging inclination towards carrying out tasks on computational
devices and digital mediums, any method that converts a task that was
previously carried out manually, to a digitized version, is always welcome.
Irrespective of the various documentation tasks that can be done online today,
there are still many applications and domains where handwritten text is
inevitable, which makes the digitization of handwritten documents a very
essential task. Over the past decades, there has been extensive research on
offline handwritten text recognition. In the recent past, most of these
attempts have shifted to Machine learning and Deep learning based approaches.
In order to design more complex and deeper networks, and ensure stellar
performances, it is essential to have larger quantities of annotated data. Most
of the databases present for offline handwritten text recognition today, have
either been manually annotated or semi automatically annotated with a lot of
manual involvement. These processes are very time consuming and prone to human
errors. To tackle this problem, we present an innovative, complete end-to-end
pipeline, that annotates offline handwritten manuscripts written in both print
and cursive English, using Deep Learning and User Interaction techniques. This
novel method, which involves an architectural combination of a detection system
built upon a state-of-the-art text detection model, and a custom made Deep
Learning model for the recognition system, is combined with an easy-to-use
interactive interface, aiming to improve the accuracy of the detection,
segmentation, serialization and recognition phases, in order to ensure high
quality annotated data with minimal human interaction.

本文介绍了一种创新的、完整的端到端流程，采用深度学习和用户交互技术，对印刷和草书英文手写手稿进行注释，该方法使用了最先进的文本识别模型构建的检测系统和自定义的深度学习模型，结合易于使用的交互式界面，旨在提高检测、分割、序列化和识别阶段的准确性，以确保高质量的注释数据，最大程度地减少人类交互。

针对英文手写体草书和印刷体的端到端交互式深度学习标注系统

An end-to-end, interactive Deep Learning based Annotation system for  cursive and print English handwritten text

Many studies on (Offline) Handwritten Text Recognition (HTR) systems have
focused on building state-of-the-art models for line recognition on small
corpora. However, adding HTR capability to a large scale multilingual OCR
system poses new challenges. This paper addresses three problems in building
such systems: data, efficiency, and integration. Firstly, one of the biggest
challenges is obtaining sufficient amounts of high quality training data. We
address the problem by using online handwriting data collected for a large
scale production online handwriting recognition system. We describe our image
data generation pipeline and study how online data can be used to build HTR
models. We show that the data improve the models significantly under the
condition where only a small number of real images is available, which is
usually the case for HTR models. It enables us to support a new script at
substantially lower cost. Secondly, we propose a line recognition model based
on neural networks without recurrent connections. The model achieves a
comparable accuracy with LSTM-based models while allowing for better
parallelism in training and inference. Finally, we present a simple way to
integrate HTR models into an OCR system. These constitute a solution to bring
HTR capability into a large scale OCR system.

本研究主要解决将手写文本识别能力添加到大规模多语言 OCR 系统中的难点，包括数据获取，效率和集成等三个方面，通过使用在线手写数据集和基于神经网络的线识别模型等方式解决了这些问题，实现了 HTR 能力在 OCR 系统中的集成。