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13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013

作者： Doswald, A. Carrino, F. Ringeval, F. Document Image and Voice Analysis group Department of Informatics University of Fribourg Fribourg Switzerland University of Applied Sciences of Western Switzerland and University of Fribourg Fribourg Switzerland

ISBN: (纸本)9783319008455

While the classification of gestures recorded with sEMG can reach very high recognition rates when the user has trained on the system, performance obtained on unknown users remains low. In this work we attempt to use advanced signal processing and pattern classification methods for improving classification performance of gestures on unknown users. Our approach is to take an existing feature set, add promising features, and use feature selection to prune poor features. For classification we use a support vector machine with a Pearson VII kernel, for which a particle swarm optimization was used to search through its parameter space. Results are presented on the NinaPro database, and show excellent results when the user is known to the system as well as a significant improvement on existing work when the user is unknown. © Springer International Publishing Switzerland 2014.

关键词： Classification (of information)

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Simple and fast geometrical descriptors for writer identification 23

Simple and fast geometrical descriptors for writer identific...

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23rd document Recognition and Retrieval 2016, DRR 2016

作者： Garz, Angelika Würsch, Marcel Fischer, Andreas Ingold, Rolf Document Image and Voice Analysis Group University of Fribourg Fribourg1700 Switzerland ICoSys Institute University of Applied Sciences and Arts Western Switzerland Fribourg1700 Switzerland

Recent advances in writer identification push the limits by using increasingly complex methods relying on sophisticated preprocessing, or the combination of already complex descriptors. In this paper, we pursue a simpler and faster approach to writer identification, introducing novel descriptors computed from the geometrical arrangement of interest points at different scales. They capture orientation distributions and geometrical relationships of script parts such as strokes, junctions, endings, and loops. Thus, we avoid a fixed set of character appearances as in standard codebook-based methods. The proposed descriptors significantly cut down processing time compared to existing methods, are simple and efficient, and can be applied out-of-the-box to an unseen dataset. Evaluations on widely-used datasets show their potential when applied by themselves, and in combination with other descriptors. Limitations of our method relate to the amount of data needed to obtain reliable models. © 2016 Society for Imaging Science and Technology.

关键词： Geometry

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DIVA-HisDB: A Precisely Annotated Large Dataset of Challenging Medieval Manuscripts

DIVA-HisDB: A Precisely Annotated Large Dataset of Challengi...

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International Workshop on Frontiers in Handwriting Recognition

作者： Foteini Simistira Mathias Seuret Nicole Eichenberger Angelika Garz Marcus Liwicki Rolf Ingold Document Image and Voice Analysis Group (DIVA) Document Image & Voice Anal. Group Univ. of Fribourg Fribourg Fribourg Switzerland

ISBN: (纸本)9781509009824

This paper introduces a publicly available historical manuscript database DIVA-HisDB for the evaluation of several document image analysis (DIA) tasks. The database consists of 150 annotated pages of three different medieval manuscripts with challenging layouts. Furthermore, we provide a layout analysis ground-truth which has been iterated on, reviewed, and refined by an expert in medieval studies. DIVA-HisDB and the ground truth can be used for training and evaluating DIA tasks, such as layout analysis, text line segmentation, binarization and writer identification. 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. An optimized state-of-the-art Convolutional Auto-Encoder (CAE) performs with around 95% accuracy, demonstrating that for this challenging layout there is much room for improvement. Finally, we show that existing text line segmentation methods fail due to interlinear and marginal text elements.

关键词： Layout Databases Benchmark testing Training Text analysis Libraries Convolution

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N-Light-N: A Highly-Adaptable Java Library for document analysis with Convolutional Auto-Encoders and Related Architectures

N-Light-N: A Highly-Adaptable Java Library for Document Anal...

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International Workshop on Frontiers in Handwriting Recognition

作者： Mathias Seuret Rolf Ingold Marcus Liwicki Document Image and Voice Analysis Group University of Fribourg Fribourg Switzerland

ISBN: (纸本)9781509009824

This paper presents a novel, highly-adaptable Java framework N-light-N, for the work with deep neural networks, especially with CAEs. While the most popular deep learning libraries focus on fast processing and high performance, they only implement the main-stream network architectures and network units. In recent research in the document domain, however, we have shown that modified networks, units, and training processes significantly improve the performance in various tasks. To enable the document research community with such capabilities, in this paper we introduce a novel, publicly available Deep Learning framework which is easy to use, adapt, and extend. Furthermore, we present successful applications for three tasks, including two in the domain of handwritten historical documents, and show how the framework can be used for adaptation, optimization, and deeper analysis.

关键词： Libraries Training Layout Machine learning Convolution Arrays Open area test sites

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Survey of artificial intelligence for card games and its application to the swiss game jass

arXiv

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arXiv 2019年

作者： Niklaus, Joel Alberti, Michele Pondenkandath, Vinaychandran Ingold, Rolf Liwicki, Marcus Document Image and Voice Analysis Group DIVA University of Fribourg Machine Learning Group Lulea University of Technology

In the last decades we have witnessed the success of applications of Artificial Intelligence to playing games. In this work we address the challenging field of games with hidden information and card games in particular. Jass is a very popular card game in Switzerland and is closely connected with Swiss culture. To the best of our knowledge, performances of Artificial Intelligence agents in the game of Jass do not outperform top players yet. Our contribution to the community is two-fold. First, we provide an overview of the current state-of-the-art of Artificial Intelligence methods for card games in general. Second, we discuss their application to the use-case of the Swiss card game Jass. This paper aims to be an entry point for both seasoned researchers and new practitioners who want to join in the Jass challenge. Copyright © 2019, The Authors. All rights reserved.

关键词： Artificial intelligence

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Historical document Synthesis with Generative Adversarial Networks

Historical Document Synthesis with Generative Adversarial Ne...

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International Conference on document analysis and Recognition Workshops (ICDARW)

作者： Vinaychandran Pondenkandath Michele Alberti Michaël Diatta Rolf Ingold Marcus Liwicki University of Fribourg Fribourg Switzerland Document Image and Voice Analysis Group (DIVA) University of Fribourg Switzerland Luleå University of Technology Lulea Sweden Machine Learning Group Luleå University of Technology Sweden

The following topics are dealt with: learning (artificial intelligence); document image processing; convolutional neural nets; text analysis; handwritten character recognition; feature extraction; neural nets; optical... 详细信息

ISBN: (纸本)9781728150550

关键词： Task analysis Training Software Gallium nitride Electronic publishing image synthesis Generators

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A Comprehensive Study of imageNet Pre-Training for Historical document image analysis

A Comprehensive Study of ImageNet Pre-Training for Historica...

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International Conference on document analysis and Recognition

作者： Linda Studer Michele Alberti Vinaychandran Pondenkandath Pinar Goktepe Thomas Kolonko Andreas Fischer Marcus Liwicki Rolf Ingold Document Image and Voice Analysis Group (DIVA) University of Fribourg Switzerland Institute of Complex Systems (iCoSys) University of Applied Sciences and Arts Western Switzerland Machine Learning Group Luleå University of Technology Sweden

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. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the imageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with imageNet. In this paper, we present a comprehensive empirical survey on the effect of imageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that imageNet pre-training has a positive effect on classification as well as content-based retrieval.

关键词： Task analysis Text analysis Training image segmentation Semantics Computer architecture image analysis

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Page Segmentation for Historical Handwritten document images Using Color and Texture Features

Page Segmentation for Historical Handwritten Document Images...

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International Workshop on Frontiers in Handwriting Recognition

作者： Kai Chen Hao Wei Jean Hennebert Rolf Ingold Marcus Liwicki DIVA (Document Image and Voice Analysis) research group University of Fribourg Switzerland HES-SO//FR University of Applied Sciences Fribourg Switzerland DFKI German Research Center for Artificial Intelligence

In this paper we present a physical structure detection method for historical handwritten document images. We considered layout analysis as a pixel labeling problem. By classifying each pixel as either periphery, background, text block, or decoration, we achieve high quality segmentation without any assumption of specific topologies and shapes. Various color and texture features such as color variance, smoothness, Laplacian, Local Binary Patterns, and Gabor Dominant Orientation Histogram are used for classification. Some of these features have so far not got many attentions for document image layout analysis. By applying an Improved Fast Correlation-Based Filter feature selection algorithm, the redundant and irrelevant features are removed. Finally, the segmentation results are refined by a smoothing post-processing procedure. The proposed method is demonstrated by experiments conducted on three different historical handwritten document image datasets. Experiments show the benefit of combining various color and texture features for classification. The results also show the advantage of using a feature selection method to choose optimal feature subset. By applying the proposed method we achieve superior accuracy compared with earlier work on several datasets, e.g., We achieved 93% accuracy compared with 91% of the previous method on the Parzival dataset which contains about 100 million pixels.

关键词： image color analysis image segmentation Layout Text analysis Histograms Accuracy Color

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Page segmentation of historical document images with convolutional autoencoders

Page segmentation of historical document images with convolu...

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International Conference on document analysis and Recognition

作者： Kai Chen Mathias Seuret Marcus Liwicki Jean Hennebert Rolf Ingold DIVA (Document Image and Voice Analysis) research group University of Fribourg Switzerland DFKI - German Research Center for Artificial Intelligence HES-SO//FR University of Applied Sciences Fribourg Switzerland

ISBN: (纸本)9781479918065

In this paper, we present an unsupervised feature learning method for page segmentation of historical handwritten documents available as color images. We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as either periphery, background, text block, or decoration. Traditional methods in this area rely on carefully hand-crafted features or large amounts of prior knowledge. In contrast, we apply convolutional autoencoders to learn features directly from pixel intensity values. Then, using these features to train an SVM, we achieve high quality segmentation without any assumption of specific topologies and shapes. Experiments on three public datasets demonstrate the effectiveness and superiority of the proposed approach.

关键词： Support vector machines Robustness image segmentation

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Labeling, Cutting, grouping: An Efficient Text Line Segmentation Method for Medieval Manuscripts

Labeling, Cutting, Grouping: An Efficient Text Line Segmenta...

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International Conference on document analysis and Recognition

作者： Michele Alberti Lars Vögtlin Vinaychandran Pondenkandath Mathias Seuret Rolf Ingold Marcus Liwicki Document Image and Voice Analysis Group (DIVA) University of Fribourg Switzerland University of Fribourg Fribourg Switzerland Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Machine Learning Group Luleå University of Technology Sweden

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 complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleed-through, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a text-line extraction step. 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%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is two-fold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising pre-processing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the high-quality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.

关键词： Semantics Task analysis image segmentation Degradation Layout image color analysis Noise reduction

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