Andreas Fischer

Andreas Fischer
Université de Fribourg · Department of Informatics

PhD

About

89
Publications
22,927
Reads
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2,321
Citations
Additional affiliations
February 2015 - present
Université de Fribourg
Position
  • Senior Researcher / Lecturer
October 2013 - February 2015
Polytechnique Montréal
Position
  • PostDoc Position
October 2012 - September 2013
Concordia University Montreal
Position
  • PostDoc Position

Publications

Publications (89)
Conference Paper
Full-text available
In the field of automatic signature verification, a major challenge for statistical analysis and pattern recognition is the small number of reference signatures per user. Score normalization, in particular, is challenged by the lack of information about intra-user variability. In this paper, we analyze several approaches to score normalization for...
Conference Paper
Full-text available
What can be done with only one enrolled real handwritten signature in Automatic Signature Verification (ASV)? Using 5 or 10 signatures for training is the most common case to evaluate ASV. In the scarcely addressed case of only one available signature for training, we propose to use modified duplicates. Our novel technique relies on a fully neuromu...
Article
Graph edit distance is a powerful and flexible method for error-tolerant graph matching. Yet it can only be calculated for small graphs in practice due to its exponential time complexity when considering unconstrained graphs. In this paper we propose a quadratic time approximation of graph edit distance based on Hausdorff matching. In a series of e...
Article
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 d...
Preprint
Full-text available
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 d...
Chapter
Handwritten signatures are of eminent importance in many business and legal activities around the world. That is, signatures have been used as authentication and verification measure for several centuries. However, the high relevance of signatures is accompanied with a certain risk of misuse. To mitigate this risk, automatic signature verification...
Conference Paper
Full-text available
In recent years, different approaches for hand- writing recognition that are based on graph representations have been proposed (e.g. graph-based keyword spotting or signature verification). This trend is mostly due to the avail- ability of novel fast graph matching algorithms, as well as the inherent flexibility and expressivity of graph data struc...
Preprint
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 signature verification rely on fixed-size representations, graphs are flexible in size and allow modeling local features as well as the global structure of the handwriting....
Article
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...
Conference Paper
Full-text available
Due to the high availability and applicability, handwritten signatures are an eminent biometric authentication measure in our life. To mitigate the risk of a potential misuse, automatic signature verification tries to distinguish between genuine and forged signatures. Most of the available signature verification approaches make use of vectorial rat...
Chapter
Full-text available
In contrast to statistical representations, graphs offer some inherent advantages when it comes to handwriting representation. That is, graphs are able to adapt their size and structure to the individual handwriting and represent binary relationships that might exist within the handwriting. We observe an increasing number of graph-based keyword spo...
Article
Full-text available
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...
Article
Full-text available
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...
Conference Paper
Full-text available
Scanned handwritten historical documents are often not well accessible due to the limited feasibility of automatic full transcriptions. Thus, Keyword Spotting (KWS) has been proposed as an alternative to retrieve arbitrary query words from this kind of documents. In the present paper, word images are represented by means of graphs. That is, a graph...
Chapter
In many public and private institutions, the digitalization of handwritten documents has progressed greatly in recent decades. As a consequence, the number of handwritten documents that are available digitally is constantly increasing. However, accessibility to these documents in terms of browsing and searching is still an issue as automatic full t...
Conference Paper
Full-text available
Keyword Spotting (KWS) offers a convenient way to improve the accessibility to historical handwritten documents by retrieving search terms in scanned document images. The approach for KWS proposed in the present paper is based on segmented word images that are represented by means of different types of graphs. The actual keyword spotting is based o...
Conference Paper
Full-text available
Keyword Spotting (KWS) improves the accessibility to handwritten historical documents by unconstrained retrievals of keywords. The proposed KWS framework operates on segmented words that are in turn represented as graphs. The actual KWS process is based on matching graphs by means of a cubic-time graph matching algorithm. Although this matching alg...
Conference Paper
Full-text available
Keyword spotting facilitates the indexation and organization of scanned historical manuscripts. In order to represent the structure and the shape of handwritten words, several graph- based approaches have been proposed recently with a view to template-based keyword spotting. Yet the high time complexity for graph matching, typically cubic-time or w...
Conference Paper
Full-text available
The present paper is concerned with a graph-based system for Keyword Spotting (KWS) in historical documents. This particular system operates on segmented words that are in turn represented as graphs. The basic KWS process employs the cubic-time bipartite matching algorithm (BP). Yet, even though this graph matching procedure is relatively efficient...
Conference Paper
Full-text available
About ten years ago, a novel graph edit distance framework based on bipartite graph matching has been introduced. This particular framework allows the approximation of graph edit distance in cubic time. This, in turn, makes the concept of graph edit distance also applicable to larger graphs. In the last decade the corresponding paper has been cited...
Article
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 i...
Article
Full-text available
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 intra-personal variability sufficient to provide an accurate verification of the individual's identity. To miti...
Article
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 substructur...
Article
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...
Conference Paper
Automatic layout analysis of historical documents has to cope with a large number of different scripts, writing supports, and digitalization qualities. Under these conditions, the design of robust features for machine learning is a highly challenging task. We use convolutional autoencoders to learn features from the images. In order to increase the...
Conference Paper
Full-text available
The term "historical documents" encompasses an enormous variety of document types considering different scripts, languages, writing supports, and degradation degrees. For automatic processing with machine learning and pattern recognition methods, it would be ideal to share labeled learning samples and trained statistical models across similar docum...
Conference Paper
Classifier ensembles aim at more accurate classifications than single classifiers. In the present paper we introduce a general approach to building structural classifier ensembles, i.e. classifiers that make use of graphs as representation formalism. The proposed methodology is based on a recent graph edit distance approximation. The major observat...
Conference Paper
Full-text available
The Sigma-Lognormal model of the Kinematic Theory of rapid human movements allows us to represent online signatures with an analytical neuromuscular model. It has been successfully used in the past to generate synthetic signatures in order to improve the performance of an automatic verification system. In this paper, we attempt for the first time t...
Conference Paper
The development of predictive tools has been commonly utilized as the most effective manner to prevent illnesses that strike suddenly. Within this context, investigations linking fine human motor control with brain stroke risk factors are considered to have a high potential but they are still in an early stage of research. The present paper analyse...
Conference Paper
Full-text available
In order to cope with the exponential time complexity of graph edit distance, several polynomial-time approximation algorithms have been proposed in recent years. The Hausdorff edit distance is a quadratic-time matching procedure for labeled graphs which reduces the edit distance to a correspondence problem between local substructures. In its origi...
Conference Paper
The basic idea of a recent graph matching framework is to reduce the problem of graph edit distance (GED) to an instance of a linear sum assignment problem (LSAP). The optimal solution for this simplified GED problem can be computed in cubic time and is eventually used to derive a suboptimal solution for the original GED problem. Yet, for large sca...
Article
The concept of graph edit distance (GED) is still one of the most flexible and powerful graph matching approaches available. Yet, exact computation of GED can be solved in exponential time complexity only. A previously introduced approximation framework reduces the computation of GED to an instance of a linear sum assignment problem. Major benefit...
Conference Paper
Exact computation of graph edit distance (GED) can be solved in exponential time complexity only. A previously introduced ap- proximation framework reduces the computation of GED to an instance of a linear sum assignment problem. Major benefit of this reduction is that an optimal assignment of nodes (including local structures) can be computed in p...
Conference Paper
Graph edit distance (GED) is a powerful and flexible graph dissimilarity model. Yet, exact computation of GED is an instance of a quadratic assignment problem and can thus be solved in exponential time complexity only. A previously introduced approximation framework re- duces the computation of GED to an instance of a linear sum assignment problem....
Conference Paper
Full-text available
A fully automatic framework has been introduced recently for neuromuscular representation of complex handwrit- ing patterns, such as gestures, signatures, and words, based on the Kinematic Theory of rapid human movements and its Sigma- Lognormal model. In this paper, we investigate the application of this framework to unconstrained whiteboard notes...
Conference Paper
Full-text available
The use of Higher-Order Singular Value Decompo- sition (HOSVD) and other tensor decomposition methods are popular in the face recognition domain, yet a direct application to handwritten character recognition has not shown promising results so far. Character recognition is commonly performed in two steps: feature extraction and classification. In th...
Conference Paper
In recent years the authors of the present paper introduced a powerful approximation framework for the graph edit distance problem. The basic idea of this approximation is to build a square cost matrix C = (cij), where each entry cij reflects the cost of a node substitution, deletion or insertion plus the matching cost arising from the local edge s...
Conference Paper
Many flexible methods for graph dissimilarity computation are based on the concept of edit distance. A recently developed approximation framework allows one to compute graph edit distances substantially faster than traditional methods. Yet, this novel procedure considers the local edge structure only during the primary optimization process. Hence,...
Conference Paper
Full-text available
Graph edit distance is a flexible and powerful measure of dissimilarity between two arbitrarily labeled graphs. Yet its application is limited by the exponential time complexity involved when matching unconstrained graphs. We have recently proposed a quadratic-time approximation of graph edit distance based on Hausdorff matching, which underestimat...
Conference Paper
With increasing computational power, the trend in unconstrained text recognition is going towards whole document processing. For this task, more sophisticated language models can be employed. One approach is to take advantage the fact that the text of a document normally deals with a specific topic and hence the word occurrence probability is biase...
Conference Paper
Full-text available
Automated reading of historical handwriting is needed to search and browse ancient manuscripts in digital libraries based on their textual content. In this paper, we present a combined system for text localization and transcription in page images. It includes flexible learning-based methods for layout analysis and handwriting recognition, which wer...
Article
Unconstrained off-line continuous handwritten text recognition is a very challenging task which has been recently addressed by different promising techniques. This work presents our latest contribution to this task, integrating neural network language models in the decoding process of three state-of-the-art systems: one based on bidirectional recur...
Article
The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need fo...
Chapter
Full-text available
This paper gives an overview of the HisDoc project, which aims at developing adaptable tools to support cultural heritage preservation by making historical documents, particularly medieval documents, electronically available for access via the Internet. HisDoc consists of three major components. The first component is image analysis. It has two mai...
Chapter
Hidden Markov models nowadays belong to the most widely used statistical models for the challenging task of handwriting recognition in document images. In this chapter, we describe the predominant application given by segmentation-free recognition of handwritten text lines in presence of large vocabularies and multiple writers. A review of the stat...
Conference Paper
Full-text available
Historical documents pose challenging problems for training handwriting recognition systems. Besides the high variability of character shapes inherent to all handwriting, the image quality can also differ greatly, for instance due to faded ink, ink bleed-through, wrinkled and stained parchment. Especially when only few learning samples are availabl...
Conference Paper
Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substanti...
Conference Paper
Unconstrained on-line handwriting recognition is typically approached within the framework of generative HMMbased classifiers. In this paper, we introduce a novel discriminative method that relies, in contrast, on explicit grapheme segmentation and SVM-based character recognition. In addition to single character recognition with rejection, bi-chara...
Conference Paper
Full-text available
Text line segmentation is one of the main parts of document image analysis, it provides crucial information for automated reading, word spotting, alignment between image and transcription, or indexing of documents. Yet it remains an open problem for handwritten historical documents because of complex layouts on the one hand, such as curved and touc...
Conference Paper
Full-text available
Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a hi...
Conference Paper
Full-text available
The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between...
Conference Paper
State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled tex...
Article
For retrieving keywords from scanned handwritten documents, we present a word spotting system that is based on character Hidden Markov Models. In an efficient lexicon-free approach, arbitrary keywords can be spotted without pre-segmenting text lines into words. For a multi-writer scenario on the IAM off-line database as well as for two single write...
Conference Paper
Full-text available
Segmenting page images into text lines is a crucial pre-processing step for automated reading of historical documents. Challenging issues in this open research field are given \eg by paper or parchment background noise, ink bleed-through, artifacts due to aging, stains, and touching text lines. In this paper, we present a novel binarization-free li...
Conference Paper
Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Curren...
Conference Paper
Full-text available
Spotting keywords in handwritten documents without transcription is a valuable method as it allows one to search, index, and classify such documents. In this paper we show that keyword spotting based on bi-directional Long Short-Term Memory (BLSTM) recurrent neural nets can successfully be applied on online handwritten documents with non-text conte...