Writer Identification Using Deep Neural Networks: Impact of Patch Size and Number of Patches

Abstract

Traditional approaches for the recognition or identification of the writer of a handwritten text image used to relay on heuristic knowledge about the shape and other features of the strokes of previously segmented characters. However, recent works have done significantly advances on the state of the art thanks to the use of various types of deep neural networks. In most of all of these works, text images are decomposed into patches, which are processed by the networks without any previous character or word segmentation. In this paper, we study how the way images are decomposed into patches impact recognition accuracy, using three publicly available datasets. The study also includes a simpler architecture where no patches are used at all – a single deep neural network inputs a whole text image and directly provides a writer recognition hypothesis. Results show that bigger patches generally lead to improved accuracy, achieving in one of the datasets a significant improvement over the best results reported so far.