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
- URL: http://arxiv.org/abs/2105.11021v1
- Date: Sun, 23 May 2021 21:17:18 GMT
- Title: 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
- Authors: Pascal Fischer, Alen Smajic, Alexander Mehler, Giuseppe Abrami
- Abstract summary: The recognition of tables consists of two main tasks, namely table detection and table structure recognition.
Recent work shows a clear trend towards deep learning approaches coupled with the use of transfer learning for the task of table structure recognition.
We present a multistage pipeline named Multi-Type-TD-TSR, which offers an end-to-end solution for the problem of table recognition.
- Score: 63.98463053292982
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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