This paper addresses a major challenge to historical research on the 19th
century. Large quantities of sources have become digitally available for the
first time, while extraction techniques are lagging behind. Therefore, we
researched machine learning (ML) models to recognise and extract complex data
structures in a high-value historical primary source, the Schematismus. It
records every single person in the Habsburg civil service above a certain
hierarchical level between 1702 and 1918 and documents the genesis of the
central administration over two centuries. Its complex and intricate structure
as well as its enormous size have so far made any more comprehensive analysis
of the administrative and social structure of the later Habsburg Empire on the
basis of this source impossible. We pursued two central objectives: Primarily,
the improvement of the OCR quality, for which we considered an improved
structure recognition to be essential; in the further course, it turned out
that this also made the extraction of the data structure possible. We chose
Faster R-CNN as base for the ML architecture for structure recognition. In
order to obtain the required amount of training data quickly and economically,
we synthesised Hof- und Staatsschematismus-style data, which we used to train
our model. The model was then fine-tuned with a smaller set of manually
annotated historical source data. We then used Tesseract-OCR, which was further
optimised for the style of our documents, to complete the combined structure
extraction and OCR process. Results show a significant decrease in the two
standard parameters of OCR-performance, WER and CER (where lower values are
better). Combined structure detection and fine-tuned OCR improved CER and WER
values by remarkable 71.98 percent (CER) respectively 52.49 percent (WER).

该论文利用机器学习模型针对历史研究的挑战进行研究，旨在提高 OCR 质量和识别数据结构的准确度。

运用结合机器学习的方法提高 19 世纪历史文献中的 OCR 质量

Improving OCR Quality in 19th Century Historical Documents Using a  Combined Machine Learning Based Approach

We introduce a new table detection and structure recognition approach named
RobusTabNet to detect the boundaries of tables and reconstruct the cellular
structure of each table from heterogeneous document images. For table
detection, we propose to use CornerNet as a new region proposal network to
generate higher quality table proposals for Faster R-CNN, which has
significantly improved the localization accuracy of Faster R-CNN for table
detection. Consequently, our table detection approach achieves state-of-the-art
performance on three public table detection benchmarks, namely cTDaR TrackA,
PubLayNet and IIIT-AR-13K, by only using a lightweight ResNet-18 backbone
network. Furthermore, we propose a new split-and-merge based table structure
recognition approach, in which a novel spatial CNN based separation line
prediction module is proposed to split each detected table into a grid of
cells, and a Grid CNN based cell merging module is applied to recover the
spanning cells. As the spatial CNN module can effectively propagate contextual
information across the whole table image, our table structure recognizer can
robustly recognize tables with large blank spaces and geometrically distorted
(even curved) tables. Thanks to these two techniques, our table structure
recognition approach achieves state-of-the-art performance on three public
benchmarks, including SciTSR, PubTabNet and cTDaR TrackB2-Modern. Moreover, we
have further demonstrated the advantages of our approach in recognizing tables
with complex structures, large blank spaces, as well as geometrically distorted
or even curved shapes on a more challenging in-house dataset.

RobusTabNet 是一种新的具有表检测和结构识别功能的方法，可检测表格的边界，并从异构文档图像中重建每个表格的细胞结构。我们提出了使用 CornerNet 作为新的区域提议网络进行表检测，并提出了基于分割和合并的表格结构识别方法。

异构文档图像中的鲁棒表格检测和结构识别

Robust Table Detection and Structure Recognition from Heterogeneous  Document Images

With the widespread use of mobile phones and scanners to photograph and
upload documents, the need for extracting the information trapped in
unstructured document images such as retail receipts, insurance claim forms and
financial invoices is becoming more acute. A major hurdle to this objective is
that these images often contain information in the form of tables and
extracting data from tabular sub-images presents a unique set of challenges.
This includes accurate detection of the tabular region within an image, and
subsequently detecting and extracting information from the rows and columns of
the detected table. While some progress has been made in table detection,
extracting the table contents is still a challenge since this involves more
fine grained table structure(rows & columns) recognition. Prior approaches have
attempted to solve the table detection and structure recognition problems
independently using two separate models. In this paper, we propose TableNet: a
novel end-to-end deep learning model for both table detection and structure
recognition. The model exploits the interdependence between the twin tasks of
table detection and table structure recognition to segment out the table and
column regions. This is followed by semantic rule-based row extraction from the
identified tabular sub-regions. The proposed model and extraction approach was
evaluated on the publicly available ICDAR 2013 and Marmot Table datasets
obtaining state of the art results. Additionally, we demonstrate that feeding
additional semantic features further improves model performance and that the
model exhibits transfer learning across datasets. Another contribution of this
paper is to provide additional table structure annotations for the Marmot data,
which currently only has annotations for table detection.

本文介绍了 TableNet—— 一种新颖的端到端深度学习模型，用于识别文档图像中的表格，并提出了一种语义规则的行提取方法，结果表明该模型在两个公开数据集（ICDAR 2013 和 Marmot Table）上都达到了最佳性能，并能通过给模型添加额外的语义特征进行性能提升，同时表明该模型对数据集的迁移学习效果良好。