While storing invoice content as metadata to avoid paper document processing
may be the future trend, almost all of daily issued invoices are still printed
on paper or generated in digital formats such as PDFs. In this paper, we
introduce the OCRMiner system for information extraction from scanned document
images which is based on text analysis techniques in combination with layout
features to extract indexing metadata of (semi-)structured documents. The
system is designed to process the document in a similar way a human reader
uses, i.e. to employ different layout and text attributes in a coordinated
decision. The system consists of a set of interconnected modules that start
with (possibly erroneous) character-based output from a standard OCR system and
allow to apply different techniques and to expand the extracted knowledge at
each step. Using an open source OCR, the system is able to recover the invoice
data in 90% for English and in 88% for the Czech set.

OCRMiner 系统使用文本分析技术与布局特征相结合的方法从扫描文档图像中提取 (半) 结构化文档的索引元数据。该系统由多个相互连接的模块组成，使用开源 OCR 系统，其英文数据召回率达 90％，捷克语数据召回率为 88％。

利用文本分析和版面特征从扫描的发票图像中提取信息

Information Extraction from Scanned Invoice Images using Text Analysis and Layout Features

Tables are information-rich structured objects in document images. While
significant work has been done in localizing tables as graphic objects in
document images, only limited attempts exist on table structure recognition.
Most existing literature on structure recognition depends on extraction of
meta-features from the PDF document or on the optical character recognition
(OCR) models to extract low-level layout features from the image. However,
these methods fail to generalize well because of the absence of meta-features
or errors made by the OCR when there is a significant variance in table layouts
and text organization. In our work, we focus on tables that have complex
structures, dense content, and varying layouts with no dependency on
meta-features and/or OCR.
We present an approach for table structure recognition that combines cell
detection and interaction modules to localize the cells and predict their row
and column associations with other detected cells. We incorporate structural
constraints as additional differential components to the loss function for cell
detection. We empirically validate our method on the publicly available
real-world datasets - ICDAR-2013, ICDAR-2019 (cTDaR) archival, UNLV, SciTSR,
SciTSR-COMP, TableBank, and PubTabNet. Our attempt opens up a new direction for
table structure recognition by combining top-down (table cells detection) and
bottom-up (structure recognition) cues in visually understanding the tables.

本研究提出了一种识别具有复杂结构，密集内容和不同布局的表格的方法，其不依赖于元特征和 OCR，并结合单元检测和交互模块来本地化细胞并预测它们与其他检测到的单元的行和列关联，同时将结构约束作为细胞检测损失函数的附加差分组件，从而将自上而下和自下而上的线索结合在一起，实现对表格的视觉理解

使用自上而下和自下而上线索的表格结构识别

Table Structure Recognition using Top-Down and Bottom-Up Cues

We introduce a simple new approach to the problem of understanding documents
where non-trivial layout influences the local semantics. To this end, we modify
the Transformer encoder architecture in a way that allows it to use layout
features obtained from an OCR system, without the need to re-learn language
semantics from scratch. We only augment the input of the model with the
coordinates of token bounding boxes, avoiding, in this way, the use of raw
images. This leads to a layout-aware language model which can then be
fine-tuned on downstream tasks.
The model is evaluated on an end-to-end information extraction task using
four publicly available datasets: Kleister NDA, Kleister Charity, SROIE and
CORD. We show that our model achieves superior performance on datasets
consisting of visually rich documents, while also outperforming the baseline
RoBERTa on documents with flat layout (NDA \(F_{1}\) increase from 78.50 to
80.42). Our solution ranked first on the public leaderboard for the Key
Information Extraction from the SROIE dataset, improving the SOTA
\(F_{1}\)-score from 97.81 to 98.17.

在 OCR 系统获取的布局特征的基础上，改进 Transformer 编码器的结构来构建布局感知的语言模型，展示在信息提取方面的出色性能。