Lecture

Text Recognition (TR) refers to the research area that focuses on retrieving
textual information from images, a topic that has seen significant advancements
in the last decade due to the use of Deep Neural Networks (DNN). However,
these solutions often necessitate vast amounts of manually labeled or synthetic
data. Addressing this challenge, Self-Supervised Learning (SSL) has gained
attention by utilizing large datasets of unlabeled data to train DNN, thereby
generating meaningful and robust representations. Although SSL was initially
overlooked in TR because of its unique characteristics, recent years have
witnessed a surge in the development of SSL methods specifically for this
field. This rapid development, however, has led to many methods being explored
independently, without taking previous efforts in methodology or comparison
into account, thereby hindering progress in the field of research. In this
context, I will present a critical and comprehensive overview of the current
state of the art, reviewing and analyzing the existing methods, comparing
their results, and highlight inconsistencies in the current literature. This
thorough analysis aims to provide general insights into the field, propose
standardizations, identify new research directions, and foster its proper
development.