DeepOtsu: Document enhancement and binarization using iterative deep learning

On the exact computation of the graph edit distance

International Workshop on Graph-Based Representations in Pattern Recognition. 
GbRPR 2017: Graph-Based Representations in Pattern Recognition pp. 242-252.

A survey on applications of bipartite graph edit distance

This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes. Secondly, we employ synchronous message passing networks to iteratively re-rank the soft correspondences to reach a matching consensus in local neighborhoods between graphs. We show, theoretically and empirically, that our message passing scheme computes a well-founded measure of consensus for corresponding neighborhoods, which is then used to guide the iterative re-ranking process. Our purely local and sparsity-aware architecture scales well to large, real-world inputs while still being able to recover global correspondences consistently. We demonstrate the practical effectiveness of our method on real-world tasks from the fields of computer vision and entity alignment between knowledge graphs, on which we improve upon the current state-of-the-art. Our source code is available under this https URL deep-graph-matching-consensus.

Deep Graph Matching Consensus

In the area of digital biometric systems, the handwritten signature plays a key role in the authentication of a person based on their original samples. In offline signature verification (OSV), several problems exist that are challenging for verification of authentic or forgery signature by the digital system. Correct signature verification improves the security of people, systems, and services. It is applied to uniquely identify an individual based on the motion of pen as up and down, signature speed, and shape of a loop. In this work, the multi-level features fusion and optimal features selection based automatic technique is proposed for OSV. For this purpose, twenty-two Gray Level Co-occurrences Matrix (GLCM) and eight geometric features are calculated from pre-processing signature samples. These features are fused by a new parallel approach which is based on a high-priority index feature (HPFI). A skewness-kurtosis based features selection approach is also proposed name skewness-kurtosis controlled PCA (SKcPCA) and selects the optimal features for final classification into forged and genuine signatures. MCYT, GPDS synthetic, and CEDAR datasets are utilized for validation of the proposed system and show enhancement in terms of Far and FRR as compared to existing methods.

Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM

Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). S+SSPR 2016: Structural, Syntactic, and Statistical Pattern Recognition pp. 553-563.

/pdf/a-novel-graph-database-for-handwritten-word-images-2f6rd4qdjp.pdf

A Novel Graph Database for Handwritten Word Images

The amount of handwritten documents that is digitally available is rapidly increasing. However, we observe a certain lack of accessibility to these documents especially with respect to searching and browsing. This paper aims at closing this gap by means of a novel method for keyword spotting in ancient handwritten documents. The proposed system relies on a keypoint-based graph representation for individual words. Keypoints are characteristic points in a word image that are represented by nodes, while edges are employed to represent strokes between two keypoints. The basic task of keyword spotting is then conducted by a recent approximation algorithm for graph edit distance. The novel framework for graph-based keyword spotting is tested on the George Washington dataset on which a state-of-the-art reference system is clearly outperformed.

/pdf/graph-based-keyword-spotting-in-historical-handwritten-3txzwa49fd.pdf

Michael Stauffer

Papers

A survey on applications of bipartite graph edit distance

A Novel Graph Database for Handwritten Word Images

Graph-Based Keyword Spotting in Historical Handwritten Documents

Keyword spotting in historical handwritten documents based on graph matching

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