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INTRODUCTION | |
Recognition (ICDAR) [289]. | |
The thesis aims to fill this gap by proposing novel methods for uncertainty | |
estimation and failure prediction (Part I), and by providing a framework for | |
benchmarking and evaluating the reliability and robustness of DU technology, | |
as close as possible to real-world requirements (Part II). | |
Table 1.1. Comparative analysis of keywords in the ICDAR 2021 proceedings. While | |
many DU subtasks are represented, there is a lack of keywords related to IA. Do note | |
that calibration is used in the context of camera calibration, and not in the context of | |
confidence estimation. | |
keyword | |
freq | |
keyword | |
freq | |
document | |
classification | |
3388 | |
242 | |
33 | |
0 | |
key information | |
56 | |
question answering | |
106 | |
layout analysis | |
223 | |
calibration/calibrate | |
temperature scaling | |
failure prediction | |
misclassification detection | |
out-of-distribution | |
OOD | |
predictive uncertainty | |
0 | |
25 | |
0 | |
In the remainder of the Introduction, I will sketch the surrounding research | |
context, followed by the problem statement and research questions, and finally | |
the outline of the thesis manuscript. | |
1.1 | |
Research Context | |
All chapters of this dissertation have been executed as part of the Baekeland | |
PhD mandate (HBC.2019.2604) with financial support of VLAIO (Flemish | |
Innovation & Entrepreneurship) and Contract.fit. The latter is a Belgian-based | |
software-as-a-service (SaaS) provider of Intelligent Document Processing (IDP) | |
drawing on innovations in DU to power their product suite (email-routing, | |
Parble), and my generous employer since 2017. | |
Some of the joint work (Chapter 5) has been partially funded by a PhD | |
Scholarship from AGAUR (2023 FI-3-00223), and the Smart Growth Operational | |
Programme under projects no. POIR.01.01.01-00-1624/20 (Hiper-OCR - an | |
innovative solution for information extraction from scanned documents) and | |
POIR.01.01.01-00-0605/19 (Disruptive adoption of Neural Language Modelling | |
for automation of text-intensive work). | |
Moreover, given that the dissertation work has been performed over a large | |
span of time, it warrants putting it in the larger context and dynamics of AI | |
innovations, the state of DU as a field, how notions of ’reliability’ have evolved | |
over time, and finally the business context. | |