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Found 17 papers, 9 papers with code
CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness
We present CAISAR, an open-source platform under active development for the characterization of AI systems' robustness and safety.
Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs
Second, we transfer the style of a collection of unlabeled historical images to these template documents while preserving their text and layout.
Optical Character Recognition (OCR)
Synthetic Data Generation
Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks
In this work, we investigate the application of trainable and spectrally initializable matrix transformations on the feature maps produced by convolution operations.
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts
We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80. 7%.
Ranked #1 on
Text-Line Extraction
on DIVA-HisDB
Improving Reproducible Deep Learning Workflows with DeepDIVA
The field of deep learning is experiencing a trend towards producing reproducible research.
Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
Jass is a very popular card game in Switzerland and is closely connected with Swiss culture.
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis
Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples.
Ranked #7 on
Image Classification
on Kuzushiji-MNIST
Leveraging Random Label Memorization for Unsupervised Pre-Training
We present a novel approach to leverage large unlabeled datasets by pre-training state-of-the-art deep neural networks on randomly-labeled datasets.
Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
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.
Are You Tampering With My Data?
We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models.
DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments
We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality.
Identifying Cross-Depicted Historical Motifs
Cross-depiction is the problem of identifying the same object even when it is depicted in a variety of manners.
A Pitfall of Unsupervised Pre-Training
Experimental results suggest that in fact, there is no correlation between the reconstruction score and the quality of features for a classification task.
Open Evaluation Tool for Layout Analysis of Document Images
This paper presents an open tool for standardizing the evaluation process of the layout analysis task of document images at pixel level.
Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks
In this paper, we present a novel approach to perform deep neural networks layer-wise weight initialization using Linear Discriminant Analysis (LDA).
A Pitfall of Unsupervised Pre-Training
Experimental results suggest that in fact, there is no correlation between the reconstruction score and the quality of features for a classification task.
PCA-Initialized Deep Neural Networks Applied To Document Image Analysis
In this paper, we present a novel approach for initializing deep neural networks, i. e., by turning PCA into neural layers.
