Offline Signature Verification via Structural Methods: Graph Edit Distance and Inkball Models

For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graphbased representations offer a promising alternative, as they are flexible in size and model the global structure of the handwriting. However, they are only rarely used for signature verification, which may be due to the high computational complexity involved when matching two graphs. In this paper, we take a closer look at two recently presented structural methods for handwriting analysis, for which efficient matching methods are available: keypoint graphs with approximate graph edit distance and inkball models. Inkball models, in particular, have never been used for signature verification before. We investigate both approaches individually and propose a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art

Graph-Based Offline Signature Verification

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. In this article, we present two recent graph-based approaches to offline signature verification: keypoint graphs with approximated graph edit distance and inkball models. We provide a comprehensive description of the methods, propose improvements both in terms of computational time and accuracy, and report experimental results for four benchmark datasets. The proposed methods achieve top results for several benchmarks, highlighting the potential of graph-based signature verification

A structural approach to offline signature verification using graph edit distance

Graphs provide a powerful representation formalism for handwritten signatures, capturing local properties as well as their relations. Yet, although introduced early for signature verification, only a few current systems rely on graph-based representations. A possible reason is the high computational complexity involved for matching two general graphs. In this paper, we introduce a novel structural approach to offline signature verification using an efficient cubic-time approximation of graph edit distance. We put forward several ways of creating, normalizing, and comparing signature graphs built from keypoints and investigate their performance on three benchmark datasets. The experiments demonstrate a promising performance of the proposed structural approach when compared with the state of the art

Graph-Based Offline Signature Verification

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. In this article, we present two recent graph-based approaches to offline signature verification: keypoint graphs with approximated graph edit distance and inkball models. We provide a comprehensive description of the methods, propose improvements both in terms of computational time and accuracy, and report experimental results for four benchmark datasets. The proposed methods achieve top results for several benchmarks, highlighting the potential of graph-based signature verification

Graph embedding for offline handwritten signature verification

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 rather than graph-based representations of the handwriting. This is rather surprising as graphs offer some inherent advantages. Graphs are, for instance, able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. Moreover, several fast graph matching algorithms have been proposed recently that allow to employ graphs also in domains with large amounts of data. The present paper proposes to use different graph embedding approaches in conjunction with a recent graph-based signature verification framework. That is, signature graphs are not directly matched with each other, but first compared with a set of predefined prototype graphs, in order to obtain a dissimilarity representation. In an experimental evaluation, we employ the proposed method on two widely used benchmark datasets. On both datasets, we empirically confirm that the learning-free graph embedding outperforms state-of-the-art methods with respect to both accuracy and runtime

Offline signature verification using structural dynamic time warping

In recent years, different approaches for handwriting 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 availability of novel fast graph matching algorithms, as well as the inherent flexibility and expressivity of graph data structures when compared to vectorial representations. That is, graphs are able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. However, the vast majority of the proposed approaches match the graphs from a global perspective only. In the present paper, we propose to match the underlying graphs from different local perspectives and combine the resulting assignments by means of Dynamic Time Warping. Moreover, we show that the proposed approach can be readily combined with global matchings. In an experimental evaluation, we employ the novel method in a signature verification scenario on two widely used benchmark datasets. On both datasets, we empirically confirm that the proposed approach outperforms state-of-theart methods with respect to both accuracy and runtime

Offline signature verification via structural methods ::graph edit distance and inkball models

For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graphbased representations offer a promising alternative, as they are flexible in size and model the global structure of the handwriting. However, they are only rarely used for signature verification, which may be due to the high computational complexity involved when matching two graphs. In this paper, we take a closer look at two recently presented structural methods for handwriting analysis, for which efficient matching methods are available: keypoint graphs with approximate graph edit distance and inkball models. Inkball models, in particular, have never been used for signature verification before. We investigate both approaches individually and propose a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art

DIVA-DAF ::a deep learning framework for historical document image analysis

Deep learning methods have shown strong performance in solving tasks for historical document image analysis. However, despite current libraries and frameworks, programming an experiment or a set of experiments and executing them can be time-consuming. This is why we propose an open-source deep learning framework, DIVA-DAF, which is based on PyTorch Lightning and specifically designed for historical document analysis. Pre-implemented tasks such as segmentation and classification can be easily used or customized. It is also easy to create one’s own tasks with the benefit of powerful modules for loading data, even large data sets, and different forms of ground truth. The applications conducted have demonstrated time savings for the programming of a document analysis task, as well as for different scenarios such as pre-training or changing the architecture. Thanks to its data module, the framework also allows to reduce the time of model training significantly

Combining graph edit distance and triplet networks for offline signature verification

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