Novel geometric features for off-line writer identification

Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features.Qatar National Research Fund through the National Priority Research Program (NPRP) No. 09-864-1-128Scopu

Estimation of Curvature and Tangent Direction by Median Filtered Differencing

. We present a new method, median filtered differencing, for estimation of tangent direction and curvature of digitised curves. On three synthetic examples and two images we show the algorithm performs successfully on both straight and curved segments even in the neighbourhood of discontinuities. 1 Introduction Curvature estimation is closely linked to a number of problems studied in connection with object recognition, eg. curve partitioning [3], corner detection [10] or extraction of salient points [15]. It is therefore not surprising that a number of curvature estimation methods have been proposed in literature; see [18] [4] [8] for recent surveys. Worring [17] recognises three classes of approaches to curvature estimation: orientation based [13] [1] [2], path based [9] [11] [10] [14] and osculating circle based [16]. The classification is based on disparate definitions of discrete curvature. The formulations widely differ, but the central underlying assumption remains similar: the ..