1 code implementation • 28 Aug 2024 • Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
To overcome this limitation, we propose the Merge-Attend-Diffuse operator, which can be plugged into different types of pretrained diffusion models used in a joint diffusion setting to improve the perceptual and semantical coherence of the generated panorama images.
2 code implementations • 27 Aug 2024 • Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
Designs and artworks are ubiquitous across various creative fields, requiring graphic design skills and dedicated software to create compositions that include many graphical elements, such as logos, icons, symbols, and art scenes, which are integral to visual storytelling.
1 code implementation • 26 Apr 2024 • Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
Document Image Binarization is a well-known problem in Document Analysis and Computer Vision, although it is far from being solved.
no code implementations • 16 Feb 2024 • Bram Vanherle, Vittorio Pippi, Silvia Cascianelli, Nick Michiels, Frank Van Reeth, Rita Cucchiara
Styled Handwritten Text Generation (HTG) has received significant attention in recent years, propelled by the success of learning-based solutions employing GANs, Transformers, and, preliminarily, Diffusion Models.
1 code implementation • 31 Oct 2023 • Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, Rita Cucchiara
Through extensive experimental evaluation on different word-level and line-level datasets of handwritten text images, we demonstrate the suitability of the proposed HWD as a score for Styled HTG.
1 code implementation • 9 Aug 2023 • Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
Recent advancements in Digital Document Restoration (DDR) have led to significant breakthroughs in analyzing highly damaged written artifacts.
no code implementations • 4 May 2023 • Vittorio Pippi, Silvia Cascianelli, Christopher Kermorvant, Rita Cucchiara
Recent advancements in Deep Learning-based Handwritten Text Recognition (HTR) have led to models with remarkable performance on both modern and historical manuscripts in large benchmark datasets.
1 code implementation • 4 Apr 2023 • Vittorio Pippi, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
In this work, we explore massive pre-training on synthetic word images for enhancing the performance on four benchmark downstream handwriting analysis tasks.
1 code implementation • CVPR 2023 • Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara
Generating synthetic images of handwritten text in a writer-specific style is a challenging task, especially in the case of unseen styles and new words, and even more when these latter contain characters that are rarely encountered during training.
no code implementations • 16 Aug 2022 • Silvia Cascianelli, Vittorio Pippi, Martin Maarand, Marcella Cornia, Lorenzo Baraldi, Christopher Kermorvant, Rita Cucchiara
With the aim of fostering the research on this topic, in this paper we present the Ludovico Antonio Muratori (LAM) dataset, a large line-level HTR dataset of Italian ancient manuscripts edited by a single author over 60 years.