Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition

Handwritten text recognition (HTR) is the research area in the field of computer vision whose objective is to transcribe the textual content of a written manuscript into a digital machine-readable format [73]. This field not only plays a key role in the current digital era of handwriting by electronic means (such as tablets) [11], but is also of paramount relevance for the preservation, indexing and dissemination of historical manuscripts that exist solely in a physical format [56]. HTR has developed considerably over the last decade owing to the emergence of Deep Learning [57], which has greatly increased its performance. However, in order to attain competitive results, these solutions usually require large volumes of manually-labelled data, which is the principal bottleneck of this method. One means by which to alleviate this problem, Self-Supervised Learning (SSL), has recently gained considerable attention from the research community [61]. SSL employs what is termed as a pretext task to leverage collections of unlabelled data for the training of neural models in order to obtain descriptive and intelligible representations [8], thus reducing the need for large amounts of labelled data [4]. The pretext tasks can be framed in different categories according to their working principle [34, 61], with the following being some of the main existing families: (i) image generation strategies [63, 46], which focus on recovering the original distribution of the data from defined distortions or corruptions; (ii) contrastive learning methods [60, 33], whose objective is to learn representative and discernible codifications of the data, and (iii) spatial context methods [27, 58], which focus on either estimating geometric transformations performed on the data [27]--i.e.