While analyzing scanned documents, handwritten text can overlay printed text.
This causes difficulties during the optical character recognition (OCR) and
digitization process of documents, and subsequently, hurts downstream NLP
tasks. Prior research either focuses only on the binary classification of
handwritten text, or performs a three-class segmentation of the document, i.e.,
recognition of handwritten, printed, and background pixels. This results in the
assignment of the handwritten and printed overlapping pixels to only one of the
classes, and thus, they are not accounted for in the other class. Thus, in this
research, we develop novel approaches for addressing the challenges of
handwritten and printed text segmentation with the goal of recovering text in
different classes in whole, especially improving the segmentation performance
on the overlapping parts. As such, to facilitate with this task, we introduce a
new dataset, SignaTR6K, collected from real legal documents, as well as a new
model architecture for handwritten and printed text segmentation task. Our best
configuration outperforms the prior work on two different datasets by 17.9% and
7.3% on IoU scores.

本篇论文研究了手写文本与印刷文本重叠造成的光学字符识别（OCR）和数字化过程中的困难，并引入了一个新的数据集和模型，以改善对重叠部分的识别性能。最佳配置的 IoU 得分相较于先前的工作，在两个不同的数据集上分别提高了 17.9% 和 7.3%。

手写和印刷文本分割：一个签名案例研究

Handwritten and Printed Text Segmentation: A Signature Case Study

Optical Character Recognition (OCR) technology finds applications in
digitizing books and unstructured documents, along with applications in other
domains such as mobility statistics, law enforcement, traffic, security
systems, etc. The state-of-the-art methods work well with the OCR with printed
text on license plates, shop names, etc. However, applications such as printed
textbooks and handwritten texts have limited accuracy with existing techniques.
The reason may be attributed to similar-looking characters and variations in
handwritten characters. Since these issues are challenging to address with OCR
technologies exclusively, we propose a post-processing approach using Natural
Language Processing (NLP) tools. This work presents an end-to-end pipeline that
first performs OCR on the handwritten or printed text and then improves its
accuracy using NLP.

提出了一种使用自然语言处理（NLP）工具的后处理方法，在手写或打印文本上先进行光学字符识别（OCR），然后使用 NLP 提高其准确性。

通过自然语言处理进行后处理以改善光学字符识别的新型流程

A Novel Pipeline for Improving Optical Character Recognition through  Post-processing Using Natural Language Processing

One of the most important senses in human life is vision, without it life is
totally filled with darkness. According to WHO globally millions of people are
visually impaired estimated there are 285 million, of whom some millions are
blind. Unfortunately, there are around 2.4 million people are blind in our
beloved country Pakistan. Human are a crucial part of society and the blind
community is a main part of society. The technologies are grown so far to make
the life of humans easier more comfortable and more reliable for. However, this
disability of the blind community would reduce their chance of using such
innovative products. Therefore, the visually impaired community believe that
they are burden to other societies and they do not capture in normal activities
separates the blind people from society and because of this believe did not
participate in the normally tasks of society . The visual impair people mainly
face most of the problems in this real-time The aim of this work is to turn the
real time world into an audio world by telling blind person about the objects
in their way and can read printed text. This will enable blind persons to
identify the things and read the text without any external help just by using
the object detection and reading system in real time. Objective of this work:
i) Object detection ii) Read printed text, using state-of-the-art (SOTA)
technology.

本文旨在将现实世界转化为音频世界，通过物体检测和阅读系统，帮助视障人士在现实时间识别物体和阅读印刷文本，从而使其能够独立完成任务。