most citedHandwritten Text Generation from Visual Archetypes

1 citations · 2 across the 5 of their papers we have counts for

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5 papers

cs.CV20231 cited

HWD: A Novel Evaluation Score for Styled Handwritten Text Generation

Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli +1

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…

cs.CV2023

How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning

Vittorio Pippi, Silvia Cascianelli, Christopher Kermorvant +1

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 benc…

cs.CV2023

Evaluating Synthetic Pre-Training for Handwriting Processing Tasks

Vittorio Pippi, Silvia Cascianelli, Lorenzo Baraldi +1

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 build…

cs.CV20231 cited

Handwritten Text Generation from Visual Archetypes

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 la…

cs.CV2022

The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition

Silvia Cascianelli, Vittorio Pippi, Martin Maarand +4

Handwritten Text Recognition (HTR) is an open problem at the intersection of Computer Vision and Natural Language Processing. The main challenges, when dealing with historical manu…