As global trends are shifting towards data-driven industries, the demand for
automated algorithms that can convert digital images of scanned documents into
machine readable information is rapidly growing. Besides the opportunity of
data digitization for the application of data analytic tools, there is also a
massive improvement towards automation of processes, which previously would
require manual inspection of the documents. Although the introduction of
optical character recognition technologies mostly solved the task of converting
human-readable characters from images into machine-readable characters, the
task of extracting table semantics has been less focused on over the years. The
recognition of tables consists of two main tasks, namely table detection and
table structure recognition. Most prior work on this problem focuses on either
task without offering an end-to-end solution or paying attention to real
application conditions like rotated images or noise artefacts inside the
document image. Recent work shows a clear trend towards deep learning
approaches coupled with the use of transfer learning for the task of table
structure recognition due to the lack of sufficiently large datasets. In this
paper we present a multistage pipeline named Multi-Type-TD-TSR, which offers an
end-to-end solution for the problem of table recognition. It utilizes
state-of-the-art deep learning models for table detection and differentiates
between 3 different types of tables based on the tables' borders. For the table
structure recognition we use a deterministic non-data driven algorithm, which
works on all table types. We additionally present two algorithms. One for
unbordered tables and one for bordered tables, which are the base of the used
table structure recognition algorithm. We evaluate Multi-Type-TD-TSR on the
ICDAR 2019 table structure recognition dataset and achieve a new
state-of-the-art.

本研究提出了一种名为 Multi-Type-TD-TSR 的多阶段流水线，可实现表格检测和结构识别的端对端解决方案，利用深度学习模型实现表格检测和区分边框的三种不同类型的表格。在 ICDAR 2019 表格结构识别数据集上进行评估，实现了新的最先进技术。

使用多阶段流程进行表格检测和表格结构识别的多类型 TD-TSR —— 从 OCR 到结构化表格表示中提取文档图像的表格

Multi-Type-TD-TSR -- Extracting Tables from Document Images using a  Multi-stage Pipeline for Table Detection and Table Structure Recognition:  from OCR to Structured Table Representations

This paper presents final results of ICDAR 2019 Scene Text Visual Question
Answering competition (ST-VQA). ST-VQA introduces an important aspect that is
not addressed by any Visual Question Answering system up to date, namely the
incorporation of scene text to answer questions asked about an image. The
competition introduces a new dataset comprising 23,038 images annotated with
31,791 question/answer pairs where the answer is always grounded on text
instances present in the image. The images are taken from 7 different public
computer vision datasets, covering a wide range of scenarios.
The competition was structured in three tasks of increasing difficulty, that
require reading the text in a scene and understanding it in the context of the
scene, to correctly answer a given question. A novel evaluation metric is
presented, which elegantly assesses both key capabilities expected from an
optimal model: text recognition and image understanding.
A detailed analysis of results from different participants is showcased,
which provides insight into the current capabilities of VQA systems that can
read. We firmly believe the dataset proposed in this challenge will be an
important milestone to consider towards a path of more robust and general
models that can exploit scene text to achieve holistic image understanding.

本文介绍了 ICDAR 2019 场景文本视觉问答比赛 (ST-VQA) 的最终结果和新数据集，包括 23038 张图像和 31791 个文本实例答案，涵盖广泛的情景。该竞赛共有 3 个递增的任务，需要理解场景中的文本并回答问题。提出了一种新的评估度量标准，旨在评估文本识别和图像理解两种关键能力。结果分析提供了 VQA 系统当前的能力洞见。此数据集标志着使用场景文本实现全面图像理解的更强大、更通用的模型的重要里程碑。