Prototype Generation (PG) techniques enhance the efficiency of the k -Nearest Neighbor ( k NN) classifier by condensing datasets through the use of specific rules. More precisely, these strategies work on the premise of merging the elements in the reference data collection to generate an alternative and more compact data assortment that substitutes the former one without remarkably affecting the recognition performance. Nevertheless, despite being widely studied in multiclass scenarios, PG is still underexplored in multilabel contexts, leading to limitations, notably in the handling of label imbalance and noise. In this regard, this work introduces a reduction framework that allows for multilabel PG methods to handle these challenges of label imbalance and noise. The proposed mechanisms comprise a selection strategy that exclusively preserves noise-free samples in the process, a mechanism to avoid severely imbalanced samples from being inadequately processed, and two new merging policies for the PG methods to generate novel samples. These enhancements are considered along with three established multilabel PG methods: Multilabel Reduction through Homogeneous Clustering, Multilabel Chen, and Multilabel Reduction through Space Partitioning. Evaluations are conducted using three k NN-based multilabel classifiers and 12 diverse datasets with different levels of label imbalance. We additionally study the performance with varying values of k under different label-noise scenarios. The results are assessed through statistical tests and indicate that our proposals outperform the original methods that disregard label imbalance, even in the presence of noise, thus validating these approaches and fostering further research in the field.

Abstract 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. This paper, therefore, seeks to consolidate the use of SSL in the field of TR, offering a critical and comprehensive overview of the current state of the art. We will review and analyze the existing methods, compare 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.

Handwritten Text Recognition (HTR) is a relevant problem in computer vision, and implies unique challenges owing to its inherent variability and the rich contextualization required for its interpretation. Despite the success of Self-Supervised Learning (SSL) in computer vision, its application to HTR has been rather scattered, leaving key SSL methodologies unexplored. This work specifically focuses on Spatial Context-based SSL. We investigate how this family of approaches can be adapted and optimized for HTR and propose new workflows that leverage the unique features of handwritten text. Our experiments demonstrate that the methods considered lead to advancements in the state-of-the-art of SSL for HTR in a number of benchmark cases. • We investigate the performance of existing spatial context-based SSL methods for HTR. • We propose new methods within this family of SSL. • We advance the state of the art in SSL for HTR. • We highlight that HTR contains rich spatial information due to font and stroke style.