Attention based End to end network for Offline Writer Identification on Word level data

arXiv:2404.07602v1 Announce Type: cross
Abstract: Writer identification due to its widespread application in various fields has gained popularity over the years. In scenarios where optimum handwriting samples are available, whether they be in the form of a single line, a sentence, or an entire page, writer identification algorithms have demonstrated noteworthy levels of accuracy. However, in scenarios where only a limited number of handwritten samples are available, particularly in the form of word images, there is a significant scope for …

abstract application arxiv attention cs.cv cs.lg data fields form handwriting identification line network offline optimum page samples type word writer