Abstract
Scholarly editions play a crucial role in humanities research, particularly in
the study of literature and historical documents. The primary objective of
these editions is to reconstruct the original text or provide insights into the
author’s intentions. Traditionally, crafting a critical edition required a life
time of dedication. However, thanks to recent advancements in deep learning
and computer vision, modern text recognition tools can now be used to ex
pedite this process. A key part of these tools is document layout analysis
(DLA), where image segmentation methods are used to detect different text
elements. Most existing DLA solutions have focused on evaluating the accu
racy of these methods, often neglecting to study the practical consequences
of method selection. In this study, we have developed a new metric, the Doc
ument Layout Error Rate (DLER), which evaluates the performance of fine
grained DLA methods within the overall pipeline. This metric helps identify
the method with the lowest error rate, thereby minimizing the manual effort
required for corrections. We applied this evaluation method to assess four
different methods and their efficacy for the DLA task in the context of David
Hume’s History of England.
the study of literature and historical documents. The primary objective of
these editions is to reconstruct the original text or provide insights into the
author’s intentions. Traditionally, crafting a critical edition required a life
time of dedication. However, thanks to recent advancements in deep learning
and computer vision, modern text recognition tools can now be used to ex
pedite this process. A key part of these tools is document layout analysis
(DLA), where image segmentation methods are used to detect different text
elements. Most existing DLA solutions have focused on evaluating the accu
racy of these methods, often neglecting to study the practical consequences
of method selection. In this study, we have developed a new metric, the Doc
ument Layout Error Rate (DLER), which evaluates the performance of fine
grained DLA methods within the overall pipeline. This metric helps identify
the method with the lowest error rate, thereby minimizing the manual effort
required for corrections. We applied this evaluation method to assess four
different methods and their efficacy for the DLA task in the context of David
Hume’s History of England.
Original language | English |
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Article number | 100606 |
Journal | Machine learning with applications |
Volume | 18 |
Pages (from-to) | 1-28 |
Number of pages | 10 |
ISSN | 2666-8270 |
DOIs | |
Publication status | Published - Dec 2024 |
MoE publication type | A1 Journal article-refereed |
Fields of Science
- 113 Computer and information sciences
- Computer vision
- Deep learning
- Document layout analysis