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20172021
most citedHistorical Document Image Segmentation with LDA-Initialized Deep Neural Networks

25 citations · 40 across the 8 of their papers we have counts for

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cs.CV2021

Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs

Lars Vögtlin, Manuel Drazyk, Vinaychandran Pondenkandath +2

We present a framework to generate synthetic historical documents with precise ground truth using nothing more than a collection of unlabeled historical images. Obtaining large lab…

cs.CV2019

Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

Michele Alberti, Angela Botros, Narayan Schuez +3

In this work, we investigate the application of trainable and spectrally initializable matrix transformations on the feature maps produced by convolution operations. While previous…

cs.CV2019

Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts

Michele Alberti, Lars Vögtlin, Vinaychandran Pondenkandath +3

This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in co…

cs.CV20199 cited

Graph-Based Offline Signature Verification

Paul Maergner, Nicholas R. Howe, Kaspar Riesen +2

Graphs provide a powerful representation formalism that offers great promise to benefit tasks like handwritten signature verification. While most state-of-the-art approaches to sig…

cs.CV2019

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Linda Studer, Michele Alberti, Vinaychandran Pondenkandath +5

Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated…

cs.CV2018

Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Paul Maergner, Vinaychandran Pondenkandath, Michele Alberti +4

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures.…