In this work, we explore massive pre-training on synthetic word images for enhancing the
performance on four benchmark downstream handwriting analysis tasks. To this end, we …

Identification of scribes from historical manuscripts has remained an equally interesting
problem for paleographers as well as the pattern classification researchers. Though …

This paper presents an end-to-end neural network system to identify writers through
handwritten word images, which jointly integrates global-context information and a …

Although current image generation methods have reached impressive quality levels, they
are still unable to produce plausible yet diverse images of handwritten words. On the …

Despite the latest transcription accuracies reached using deep neural network architectures,
handwritten text recognition still remains a challenging problem, mainly because of the large …

Identifying writers using their handwriting is particularly challenging for a machine, given that
a person’ s writing can serve as their distinguishing characteristic. The process of …

Training machines to synthesize diverse handwritings is an intriguing task. Recently, RNN-
based methods have been proposed to generate stylized online Chinese characters …

Generating synthetic images is an art which emulates the natural process of image
generation in a closest possible manner. In this work, we exploit such a framework for data …

Styled Handwritten Text Generation (Styled HTG) is an important task in document analysis,
aiming to generate text images with the handwriting of given reference images. In recent …

Abstract Handwritten Word Recognition and Spotting is a challenging field dealing with
handwritten text possessing irregular and complex shapes. The design of deep neural …