Search Results for author:
Found 24 papers, 13 papers with code
Zero-Shot Paragraph-level Handwriting Imitation with Latent Diffusion Models
The imitation of cursive handwriting is mainly limited to generating handwritten words or lines.
A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches
We evaluate them on different Document Image Binarization Contest (DIBCO) datasets and obtain very heterogeneous results.
Combining OCR Models for Reading Early Modern Printed Books
Moreover, we developed a system using local font group recognition in order to combine the output of multiple font recognition models, and show that while slower, this approach performs better not only on text lines composed of multiple fonts but on the ones containing a single font only as well.
WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion Models
Our proposed method is able to generate realistic word image samples from different writer styles, by using class index styles and text content prompts without the need of adversarial training, writer recognition, or text recognition.
Ranked #1 on
HTR
on IAM
Writer Retrieval and Writer Identification in Greek Papyri
The analysis of digitized historical manuscripts is typically addressed by paleographic experts.
TorMentor: Deterministic dynamic-path, data augmentations with fractals
We propose the use of fractals as a means of efficient data augmentation.
Ranked #1 on
No real Data Binarization
on DIBCO and H_DIBCO 2009
A Survey of Historical Document Image Datasets
However, because of the very large variety of the actual data (e. g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task.
SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators
As of recent generative adversarial networks have allowed for big leaps in the realism of generated images in diverse domains, not the least of which being handwritten text generation.
ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments
In particular, we investigate the performance of large-scale retrieval of historical document fragments in terms of style and writer identification.
Proof of Concept: Automatic Type Recognition
The type used to print an early modern book can give scholars valuable information about the time and place of its production as well as its producer.
The Notary in the Haystack -- Countering Class Imbalance in Document Processing with CNNs
In classification, notarial instruments are distinguished from other documents, while the notary sign is separated from the certificate in the segmentation task.
Re-ranking for Writer Identification and Writer Retrieval
We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available.
Spatio-Temporal Handwriting Imitation
Then, a method for online handwriting synthesis is used to produce a new realistic-looking text primed with the online input sequence.
ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents
This competition investigates the performance of large-scale retrieval of historical document images based on writing style.
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.
Deep Generalized Max Pooling
Global pooling layers are an essential part of Convolutional Neural Networks (CNN).
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
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.
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.
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).
Convolutional Neural Networks for Page Segmentation of Historical Document Images
This paper presents a Convolutional Neural Network (CNN) based page segmentation method for handwritten historical document images.
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.
DIVA-HisDB: A Precisely Annotated Large Dataset of Challenging Medieval Manuscripts
Layout analysis results of several representative baseline technologies are also presented in order to help researchers evaluate their methods and advance the frontiers of complex historical manuscripts analysis.
