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Fischer Andreas

Ordentlicher Professor FH

Main skills

Ordentlicher Professor FH

Phone: +41 26 429 67 34

Desktop: HEIA_D20.07

Haute école d'ingénierie et d'architecture de Fribourg
Boulevard de Pérolles 80, 1700 Fribourg, CH

BSc HES-SO en Informatique - Haute école d'ingénierie et d'architecture de Fribourg

Algorithms and Data Structures
Formal Languages
IT Project Management

Ongoing

Automatic handwriting recognition for tax form validator (2017-2020)

Role: Main Applicant

Financement: TAINA Technology

Description du projet :

2017-2020, TAINA Technology

Research team within HES-SO: Fischer Andreas

Statut: Ongoing

Automatic translation from Swiss German to High German (2018-2021)

Role: Main Applicant

Financement: Swisscom

Description du projet :

2018-2021, Swisscom

Research team within HES-SO: Fischer Andreas

Statut: Ongoing

Towards graph-based keyword spotting in historical Vietnamese steles (2020-2021)

Role: Main Applicant

Financement: Hasler Foundation

Description du projet :

2020-2021, Hasler Foundation

Research team within HES-SO: Fischer Andreas

Statut: Ongoing

2023

Universality of Büchi automata :

Scientific paper ArODES

analysis with graph neural networks

Christophe Stammet, Ulrich Ultes-Nitsche, Andreas Fischer

IEEE Access, 2023, 11, 140993 - 141007

Link to the publication

Summary:

The universality check of Büchi automata is a foundational problem in automata-based formal verification, closely related to the complementation problem, and is known to be PSPACE-complete. This article introduces a novel approach for creating labelled datasets of Büchi automata concerning their universality. We start with small automata, where the universality check can still be algorithmically performed within a reasonable timeframe, and then apply transformations that provably preserve (non-)universality while increasing their size. This approach enables the generation of large datasets of labelled Büchi automata without the need for an explicit and computationally intensive universality check. We subsequently employ these generated datasets to train Graph Neural Networks (GNNs) for the purpose of classifying automata with respect to their (non-)universality. The classification results indicate that such a network can learn patterns related to the behaviour of Büchi automata that facilitate the recognition of universality. Additionally, our results on randomly generated automata, which were not constructed using the transformation techniques, demonstrate the network’s potential in classifying Büchi automata with respect to universality, extending its applicability beyond cases generated using a specific technique.

A hybrid deep learning approach to keyword spotting in vietnamese stele images

Scientific paper ArODES

Anna Scius-Bertrand, Marc Bui, Andreas Fischer

Informatica, 47, 3, 361-372

Link to the publication

Summary:

In order to access the rich cultural heritage conveyed in Vietnamese steles, automatic reading of stone engravings would be a great support for historians, who are analyzing tens of thousands of stele images. Approaching the challenging problem with deep learning alone is difficult because the data-driven models require large representative datasets with expert human annotations, which are not available for the steles and costly to obtain. In this article, we present a hybrid approach to spot keywords in stele images that combines data-driven deep learning with knowledge-based structural modeling and matching of Chu Nom characters. The main advantage of the proposed method is that it is annotation-free, i.e. no human data annotation is required. In an experimental evaluation, we demonstrate that keywords can be successfully spotted with a mean average precision of more than 70% when a single engraving style is considered.

2022

Self-rule to multi-adapt :

Scientific paper ArODES

generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection

Christian Abbet, Linda Studer, Andreas Fischer, Heather Dawson, Inti Zlobec, Behzad Bozorgtabar, Jean-Philippe Thiran

Medical Image Analysis, 2022, vol. 79, article no. 102473

Link to the publication

Summary:

Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to tissue stainings, types, and textures variations. Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets. In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets. SRMA can effectively transfer the discriminative knowledge obtained from a few labeled source domain’s data to a new target domain without requiring additional tissue annotations. Our method harnesses both domains’ structures by capturing visual similarity with intra-domain and cross-domain self-supervision. Moreover, we present a generalized formulation of our approach that allows the framework to learn from multiple source domains. We show that our proposed method outperforms baselines for domain adaptation of colorectal tissue type classification in single and multi-source settings, and further validate our approach on an in-house clinical cohort. The code and trained models are available open-source: https://github.com/christianabbet/SRA.

2021

Learning graph edit distance by graph neural networks

Scientific paper ArODES

Pau Riba, Josep Lladòs, Alicia Fornés, Andreas Fischer

Pattern Recognition, 2021, vol. 120, article no. 108132

Link to the publication

Summary:

The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words i.e. keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset.

Transcription alignment of historical vietnamese manuscripts without human-annotated learning samples

Scientific paper ArODES

Ann Scius-Bertrand, Michael Jungo, Beat Wolf, Andreas Fischer, Marc Bui

Applied Sciences, 2021, vol. 11, no. 11, article no. 4894

Link to the publication

Summary:

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

The VCG tool :

Book chapter ArODES

knowledge base and methods

Andreas Fischer, Michael Keller

AlpLinkBioEco (2021). Creating Bio-based Value in the Alpine Space. Interreg Alpine Space. (pp. 20-26). 2021, Interred Alpine Space : x

Link to the publication

Bio-based business opportunities unearthed :

Scientific paper ArODES

the VCG software tool

Michael Keller, Andreas Fischer, Dorian Wessely, Ashna Mudaffer

Working Paper, February 2021, HES-SO//FR HEIA-FR, iCoSys, PICC, INNOSQUARE, Business Upper Austria,

Link to the publication

CITIZEN PARTICIPATION & DIGITAL TOOLS TO IMPROVE PEDESTRIAN MOBILITY IN CITIES

Scientific paper

Sandoz Romain, Ertz Olivier, Scius-Bertrand Anna, Fischer Andreas, Hüsser Olivier, Ghorbel Hatem

The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 , vol. XLVI-4/W1-2021, pp. 29-34

Link to the publication

Summary:

In this work, we present a framework supported by mobile and web apps and able to propose personalized pedestrian routes that match
user mobility profile considering mobility impediments factors. We explain how these later have been defined using a pedestrian-
centric approach based on travel experiences as perceived in the field by senior citizens. Through workshops, six main factors that may
influence pedestrian route choices were revealed: passability, obstacle in path, surface problem, security, sidewalk width, slope. These
categories were used to build digital tools and guide a citizen participatory approach to collect geolocated points of obstacle documented
with walkability information (picture, category, impact score, free comment). We also involved citizens to evaluate these information
and especially senior referents for validation. Finally we present how we connect these points of obstacle with a pedestrian network
based on OpenStreetMap to configure a routing cost function. The framework has been partially deployed in 2020 with limited people
due to the pandemic. Nonetheless, we share lessons learned from interaction with citizens in the design of such a framework whose
underlying workflow is reproducible. We plan to further assess its relevance and sustainability in the future.

2020

Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends

Book ArODES

Andreas Fischer, Marcus Liwicki

2020, New Jersey : World Scientific, 268 p.

Link to the publication

Summary:

In recent years, libraries and archives all around the world have increased their efforts to digitize historical manuscripts. To integrate the manuscripts into digital libraries, pattern recognition and machine learning methods are needed to extract and index the contents of the scanned images. The unique compendium describes the outcome of the HisDoc research project, a pioneering attempt to study the whole processing chain of layout analysis, handwriting recognition, and retrieval of historical manuscripts. This description is complemented with an overview of other related research projects, in order to convey the current state of the art in the field and outline future trends. This must-have volume is a relevant reference work for librarians, archivists and computer scientists.

Introduction

Book chapter ArODES

Andreas Fischer, Marcus Liwicki, Rolf Ingold

Dans Fischer, Andreas, Ingold, Rolf, Liwicki, Marcus, Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends (8 p.). 2020, New Jersey : World Scientific

Link to the publication

Summary:

Document image analysis or document recognition refers to the process of extracting valuable information from document images. Although a few optical character reading systems were already available in the 1970’s, the fundamental research activities on this challenging task has mainly emerged with the development of the scanner technologies in the 1980’s, which allowed affordable document image acquisition. At that time, the main applications were focused on office automation and the interpretation of printed material…

Automatic handwriting recognition in historical documents

Book chapter ArODES

Andreas Fischer

Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends (pp. 67-80). 2020, New Jersey : World Scientific

Link to the publication

IAM-HistDB a dataset of handwritten historical documents

Book chapter ArODES

Andreas Fischer

Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends (pp. 11-23). 2020, New Jersey : World Scientific

Link to the publication

Conclusions and future trends

Book chapter ArODES

Andreas Fischer, Marcus Liwicki, Rolf Ingold

Handwritten historical document analysis, recognition, and retrieval - state of the art and future trends (pp. 249-251). 2020, New Jersey : World Scientific

Link to the publication

Taking tumour budding to the next frontier :

Scientific paper ArODES

a post International Tumour Budding Consensus Conference (ITBCC) 2016 review

Linda Studer, Annika Blank, John-Melle Bokhorst, Iris D. Nagtegaal, Inti Zlobec, Alessandro Lugli, Andreas Fischer, Heather Dawson

Histopathology, 2020, vol. 78, no. 4, pp. 476-484

Link to the publication

Summary:

Tumour budding in colorectal cancer, defined as single tumour cells or small clusters containing four or fewer tumour cells, is a robust and independent biomarker of aggressive tumour biology. On the basis of published data in the literature, the evidence is certainly in favour of reporting tumour budding in routine practice. One important aspect of implementing tumour budding has been to establish a standardised and evidence-based scoring method, as was recommended by the International Tumour Budding Consensus Conference (ITBCC) in 2016. Further developments have aimed at establishing methods for automated tumour budding assessment. A digital approach to scoring tumour buds has great potential to assist in performing an objective budding count but, like the manual consensus method, must be validated and standardised. The aim of the present review is to present general considerations behind the ITBCC scoring method, and a broad overview of the current situation and challenges regarding automated tumour budding detection methods.

2019

Modeling 3D movements with the kinematic theory of rapid human movements

Book chapter ArODES

Andreas Fischer, Roman Schindler, Manuel Bouillon, Réjean Plamondon

Dans Ferrer, Miguel Ángel, Marcelli, Angelo, Plamondon, Réjean, The lognormality principle and its applications in e-security, e-learning and e-health (pp. 327-342). 2019, New Jersey : World Scientific

Link to the publication

Summary:

The Kinematic Theory of rapid human movements analytically describes pen tip movements as a sequence of elementary strokes with lognormal speed. The theory has been confirmed in a large number of experimental evaluations, achieving a high reconstruction quality when compared with observed trajectories and providing pertinent features for biomedical applications as well as biometric identification. So far, the Kinematic Theory has focused on one-dimensional movements with the Delta-Lognormal model and on two-dimensional movements with the Sigma-Lognormal model. In this chapter, we present a model for movements in three dimensions, which naturally extends the Sigma-Lognormal approach. We evaluate our method on two action recognition datasets and an air-writing dataset, demonstrating a high reconstruction quality for modelling rapid 3D movements in all cases.

Combining graph edit distance and triplet networks for offline signature verification

Scientific paper ArODES

Paul Maergner, Vinaychandran Pondenkandath, Michele Alberti, Marcus Liwicki, Kaspar Riesen, Rolf Ingold, Andreas Fischer

Pattern Recognition Letters, 2019, vol. 125, pp. 527-533

Link to the publication

Summary:

Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures. A combination of complementary writer models can make it more difficult for an attacker to deceive the verification system. In this work, we propose to combine a recent structural approach based on graph edit distance with a statistical approach based on deep triplet networks. The combination of the structural and statistical models achieve significant improvements in performance on four publicly available benchmark datasets, highlighting their complementary perspectives.

2018

On the impact of using utilities rather than costs for graph matching

Scientific paper ArODES

Kaspar Riesen, Andreas Fischer, Horst Bunke

Neural Processing Letters, 2018, vol. 48, pp. 691-707

Link to the publication

Summary:

The concept of graph edit distance constitutes one of the most flexible graph matching paradigms available. The major drawback of graph edit distance, viz. the exponential time complexity, has been recently overcome by means of a reformulation of the edit distance problem to a linear sum assignment problem. However, the substantial speed up of the matching is also accompanied by an approximation error on the distances. Major contribution of this paper is the introduction of a transformation process in order to convert the underlying cost model into a utility model. The benefit of this transformation is that it enables the integration of additional information in the assignment process. We empirically confirm the positive effects of this transformation on five benchmark graph sets with respect to the accuracy and run time of a distance based classifier.

Keyword spotting in historical handwritten documents based on graph matching

Scientific paper ArODES

Michael Stauffer, Kaspar Riesen, Andreas Fischer

Pattern Recognition, 2018, vol. 81 pp. 240-253

Link to the publication

Summary:

In the last decades historical handwritten documents have become increasingly available in digital form. Yet, the accessibility to these documents with respect to browsing and searching remained limited as full automatic transcription is often not possible or not sufficiently accurate. This paper proposes a novel reliable approach for template-based keyword spotting in historical handwritten documents. In particular, our framework makes use of different graph representations for segmented word images and a sophisticated matching procedure. Moreover, we extend our method to a spotting ensemble. In an exhaustive experimental evaluation on four widely used benchmark datasets we show that the proposed approach is able to keep up or even outperform several state-of-the-art methods for template- and learning-based keyword spotting.

Graph-based keyword spotting in historical manuscripts using Hausdorff edit distance

Scientific paper ArODES

Mohammad Reza Ameri, Michael Stauffer, Kaspar Riesen, Tien D. Bui, Andreas Fischer

Pattern Recognition Letters,

Link to the publication

Summary:

Keyword spotting enables content-based retrieval of scanned historical manuscripts using search terms, which, in turn, facilitates the indexation in digital libraries. Recent approaches include graph-based representations that capture the complex structure of handwriting. However, the high representational power of graphs comes at the cost of high computational complexity for graph matching. In this article, we investigate the potential of Hausdorff edit distance (HED) for keyword spotting. It is an efficient quadratictime approximation of the graph edit distance. In a comprehensive experimental evaluation with four types of handwriting graphs and four benchmark datasets (George Washington, Parzival, Botany, and Alvermann Konzilsprotokolle), we demonstrate a strong performance of the proposed HED-based method when compared with the state of the art, both, in terms of precision and speed.

Filters for graph-based keyword spotting in historical handwritten documents

Scientific paper ArODES

Michael Stauffer, Andreas Fischer, Kaspar Riesen

Pattern Recognition Letters,

Link to the publication

Summary:

The accessibility to handwritten historical documents is often constrained by the limited feasibility of automatic full transcriptions. Keyword Spotting (KWS), that allows to retrieve arbitrary query words from documents, has been proposed as alternative. In the present paper, we make use of graphs for representing word images. The actual keyword spotting is thus based on matching a query graph with all documents graphs. However, even with relative fast approximation algorithms the shear amount of matchings might limit the practical application of this approach. For this reason we present two novel filters with linear time complexity that allow to substantially reduce the number of graph matchings actually required. In particular, these filters estimate a graph dissimilarity between a query graph and all document graphs based on their node and edge distribution in a polar coordinate system. Eventually, all graphs from the document with distributions that differ to heavily from the query’s node/edge distribution are eliminated. In an experimental evaluation on four different historical documents, we show that about 90% of the matchings can be omitted, while the KWS accuracy is not negatively affected.

Dynamic signature verification system based on one real signature

Scientific paper ArODES

Moises Diaz, Andreas Fischer, Miguel A. Ferrer, Réjean Plamondon

IEEE Transactions on Cybernetics, 2018, vol. 48, no. 1, pp. 228-239

Link to the publication

Summary:

The dynamic signature is a biometric trait widely used and accepted for verifying a person's identity. Current automatic signature-based biometric systems typically require five, ten, or even more specimens of a person's signature to learn intrapersonal variability sufficient to provide an accurate verification of the individual's identity. To mitigate this drawback, this paper proposes a procedure for training with only a single reference signature. Our strategy consists of duplicating the given signature a number of times and training an automatic signature verifier with each of the resulting signatures. The duplication scheme is based on a sigma lognormal decomposition of the reference signature. Two methods are presented to create human-like duplicated signatures: the first varies the strokes' lognormal parameters (stroke-wise) whereas the second modifies their virtual target points (target-wise). A challenging benchmark, assessed with multiple state-of-the-art automatic signature verifiers and multiple databases, proves the robustness of the system. Experimental results suggest that our system, with a single reference signature, is capable of achieving a similar performance to standard verifiers trained with up to five signature specimens.

2017

A user-centered segmentation method for complex historical manuscripts based on document graphs

Scientific paper ArODES

Angelika Garz, Mathias Seuret, Andreas Fischer, Rolf Ingold

IEEE Transactions on Human-Machine Systems, 2017, vol. 47, no. 2, pp- 181-193

Link to the publication

Summary:

In historical manuscripts, humans can detect handwritten words, lines, and decorations with lightness even if they do not know the language or the script. Yet for automatic processing this task has proven elusive, especially in the case of handwritten documents with complex layouts, which is why semiautomatic methods that integrate the human user into the process are needed. In this paper, we introduce a user-centered segmentation method based on document graphs and scribbling interaction. The graphs capture a sparse representation of the document's structure that can then be edited by the user with a stylus on a touch-sensitive screen. We evaluate the proposed method on a newly introduced database of historical manuscripts with complex layout and demonstrate, first, that the document graphs are already close to the desired segmentation and, second, that scribbling allows a natural and efficient interaction.

Signature verification based on the kinematic theory of rapid human movements

Scientific paper ArODES

Andreas Fischer, Réjean Plamondon

IEEE Transactions on Human-Machine Systems, 207, vol. 47, no. 2, pp. 169-180

Link to the publication

Summary:

When using tablet computers, smartphones, or digital pens, human users perform movements with a stylus or their fingers that can be analyzed by the kinematic theory of rapid human movements. In this paper, we present a user-centered system for signature verification that performs such a kinematic analysis to verify the identity of the user. It is one of the first systems that is based on a direct comparison of the elementary neuromuscular strokes which are detected in the handwriting. Taking into account the number of strokes, their similarity, and their timing, the string edit distance is employed to derive a dissimilarity measure for signature verification. On several benchmark datasets, we demonstrate that this neuromuscular analysis is complementary to a well-established verification using dynamic time warping. By combining both approaches, our verifier is able to outperform current state-of-the-art results in on-line signature verification.

Improved quadratic time approximation of graph edit distance by combining Hausdorff matching and greedy assignment

Scientific paper ArODES

Andreas Fischer, Kaspar Riesen, Horst Bunke

Pattern Recognition Letters, 2017, vol. 87, no. 1, pp. 55-62

Link to the publication

Summary:

Approximation of graph edit distance in polynomial time enables us to compare large, arbitrarily labeled graphs for structural pattern recognition. In a recent approximation framework, bipartite graph matching (BP) has been proposed to reduce the problem of edit distance to a cubic-time linear sum assignment problem (LSAP) between local substructures. Following the same line of research, first attempts towards quadratic-time approximation have been made recently, including a lower bound based on Hausdorff matching (Hausdorff Edit Distance) and an upper bound based on greedy assignment (Greedy Edit Distance). In this paper, we compare the two approaches and derive a novel upper bound (BP2) which combines advantages of both. In an experimental evaluation on the IAM graph database repository, we demonstrate that the proposed quadratic-time methods perform equally well or, quite surprisingly, in some cases even better than the cubic-time method.

2016

Simple and fast geometrical descriptors for writer identification

Scientific paper ArODES

Angelika Garz, Marcel Würsch, Andreas Fischer, Rolf Ingold

Electronic Imaging, 2016, vol. 28

Link to the publication

Summary:

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.

2023

Impact of the ground truth quality for handwriting recognition

Conference ArODES

Michael Jungo, Lars Vötglin, Atefeh Fakhari, Nathan Wegmann, Rolf Ingold, Andreas Fischer, Anna Scius-Bertrand

SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology, 7-8 December 2023, Ho Chi Minh, Vietnam

PEOPLE@HES-SO Directory and Skills inventory

Fischer Andreas

Ordentlicher Professor FH

Main skills

Pattern Recognition

Applied machine learning

Document Analysis

Handwriting Recognition

Natural Language Processing

Graph Matching

Geometric Deep Learning

Ordentlicher Professor FH

PEOPLE@HES-SO
Directory and Skills inventory