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

Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images

There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio

Writer identification using curvature-free features

Feature engineering takes a very important role in writer identification which has been widely studied in the literature. Previous works have shown that the joint feature distribution of two properties can improve the performance. The joint feature distribution makes feature relationships explicit instead of roping that a trained classifier picks up a non-linear relation present in the data. In this paper, we propose two novel and curvature-free features: run-lengths of local binary pattern (LBPruns) and cloud of line distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized and gray scale images. The COLD feature is the joint distribution of the relation between orientation and length of line segments obtained from writing contours in handwritten documents. Our proposed LBPruns and COLD are textural-based curvature-free features and capture the line information of handwritten texts instead of the curvature information. The combination of the LBPruns and COLD features provides a significant improvement on the CERUG data set, handwritten documents on which contain a large number of irregular-curvature strokes. The results of proposed features evaluated on other two widely used data sets (Firemaker and IAM) demonstrate promising results

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

Offline Text-Independent Writer Identification based on word level data

This paper proposes a novel scheme to identify the authorship of a document based on handwritten input word images of an individual. Our approach is text-independent and does not place any restrictions on the size of the input word images under consideration. To begin with, we employ the SIFT algorithm to extract multiple key points at various levels of abstraction (comprising allograph, character, or combination of characters). These key points are then passed through a trained CNN network to generate feature maps corresponding to a convolution layer. However, owing to the scale corresponding to the SIFT key points, the size of a generated feature map may differ. As an alleviation to this issue, the histogram of gradients is applied on the feature map to produce a fixed representation. Typically, in a CNN, the number of filters of each convolution block increase depending on the depth of the network. Thus, extracting histogram features for each of the convolution feature map increase the dimension as well as the computational load. To address this aspect, we use an entropy-based method to learn the weights of the feature maps of a particular CNN layer during the training phase of our algorithm. The efficacy of our proposed system has been demonstrated on two publicly available databases namely CVL and IAM. We empirically show that the results obtained are promising when compared with previous works