Chinese Signature Research Articles

Is There a Difference between Paper and Electronic Chinese Signatures?

The purpose of this study is to investigate whether there are differences in handwritten Chinese signatures on different media including paper and electronic devices. Participants were asked to sign specified names on various types of media and the signatures were scanned or saved digitally for subsequent analysis. In this study, using convolutional neural networks and Siamese neural networks as classifiers and comparators, the performance plunge is revealed and thus considerable dissimilarity between the signatures on different media is implied. To further explore this, cubic Bézier curves are fitted to the signatures using the least square method for quantitative statistical analysis. By analyzing the visual changes in the morphology of strokes, several features of signatures are selected and computed, and the paired t‐test and the Wilcoxon signed‐rank test are implemented, which provides a deeper substantiation and explanation of the findings.

Handwritten Chinese signature detection with simple Copy–Paste augmentation on power plants technical documents

Handwritten Chinese signature detection with simple Copy–Paste augmentation on power plants technical documents

Synchronous spatio-temporal signature verification via Fusion Triplet Supervised Network

Handwritten signature has been regarded as one of the most accepted and practical means of person verification since ancient times. Unlike existing methods to trace the signer only considering a single spatial or temporal information, in this paper, we propose to combine two forms of signature data (stroke images and sensor signals) to realize person verification using the novel Fusion Triplet Supervised Network (fuseTSN). First, the Electronic Handwritten Signature Acquisition System 1.0 (EHS-AS 1.0) connected to the Wacom tablet is developed to construct a synchronous static–dynamic Chinese signature dataset (SynCS) and to transform existing dynamic signature datasets into static–dynamic datasets. Then, the hybrid feature extractor (ResCNN-BiLSTM with attention) as the backbone is embedded into the fuseTSN model, mapping the images and time-series signal into a common hyperspace. Furthermore, the similarity of the input [Anchor, Positive, Test] is measured to produce the final output. Finally, the proposed fuseTSN is tested on three public datasets and also evaluated on our SynCS dataset comprised of 24000 unique triplet label samples. The results show that the accuracy of the fuseTSN model is at least 5.01% higher than that of a unimodal system with only stroke image or sensor data on the SynCS dataset and outperforms the current state-of-the-art multimodal methods for writer-independence (WI) verification on public datasets. In addition, we present the fuseTSN’s reliable judgment basis for spatial strokes in the heat-map and further find that the combination of time attributes (OA and OE) can significantly improve the model performance.

A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification

Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese neural network fails to represent the writers’ writing style fully and suffers from low performance when the distribution of positive and negative handwritten signature samples is unbalanced. To address this issue, this study proposes a two-stage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously, and (b) utilizing the Focal Loss to deal with the extreme imbalance between positive and negative offline signatures. Experimental results on four challenging handwritten signature datasets with different languages demonstrate that compared with state-of-the-art models, our proposed model achieves better performance. Furthermore, this study tries to extend the proposed model to the Chinese signature dataset in the real environment, which is a significant attempt in the field of Chinese signature identification.

Genomic correlates of programmed cell death ligand 1 (PD-L1) expression in Chinese lung adenocarcinoma patients

BackgroundAlthough PD-L1 expression is a crucial predictive biomarker for immunotherapy, it can be influenced by many factors.MethodsA total of 248 Chinese patients with lung adenocarcinoma was retrospectively identified. Data for clinical features, gene alternations, signaling pathways and immune signatures was analyzed among negative expression group (TPS < 1%, n = 124), intermediate expression group (1% ≤ TPS < 50%, n = 93), and high expression group (TPS ≥ 50%, n = 38). Clinical outcomes among different expression groups were also evaluated from public database.ResultsFirstly, high tumor mutation burden was significantly associated with high PD-L1 expression in these Chinese patients with lung adenocarcinoma. In addition, gene alternations including TP53, PRKDC, KMT2D, TET1 and SETD2 apparently occurred in high PD-L1 expression group. Moreover, pathway analysis showed that mutations involving in DDR pathway, TP53 pathway, cell-cycle pathway and NOTCH pathway were obviously varied among three PD-L1 expression groups. Besides, most of patients in high PD-L1 expression group from TCGA database were determined as high-grade immune subtypes (C2-C4), showing significant higher proportions of IFN-gamma, CD8+ T-cells, NK cells, NK CD56 dim cells, Th1 cells, Th2 cells (P < 0.0001). Moreover, SETD2 mutation slightly correlated with overall survival from MSKCC cohort (HR 1.92 [95%CI 0.90–4.10], P = 0.085), and the percentage of IFN-gamma was significantly higher in SETD2 mutant group than in wild-type group (P < 0.01).ConclusionsThis study illustrated in-depth genomic correlates of PD-L1 expression in Chinese lung adenocarcinoma patients and relevant immune signatures from public database, which might interpret more potential molecular mechanisms for immunotherapy in NSCLC.

Off-line Chinese Signature Verification Using Convolutional Neural Network

Background: The unique individual biological characteristics is used for identification in biometrics, which is safe and difficult to forge. Therefore, it can help to enhance the safety of access control system. Since the developing of modern information science and technology, computerbased signature verification system enables signature verification more efficiently and automatically in comparison with traditional human identification method. Methods: In order to improve the accuracy of Chinese signature verification, an off-line Chinese signature verification method based on deep convolutional neural network is proposed. First, the machine learning library Tensorflow is build, and the volunteers are invited to establish the offline Chinese signature dataset. Second, the dataset is pre-processed, including denoising, binarization and size normalization. Finally, three different CNN architectures (AlexNet, GoogleNet, VGGNet) are adopted to implement the signature verification. Results: Experimental results show that the performance of AlexNet is better than that of the other two convolutional neural network architectures, the accuracy of classification has been up to 99.77%, and verification rate is 87.5%. Conclusion: Compared with the traditional offline Chinese signature recognition method, the method based on the convolutional neural network Alex Net-f is better than other methods to some extent, and avoids the complicated feature engineering.

Offline Signature Recognition u sing Pretrained Convolution Neural Network Model

Offline Signature recognition plays an important role in Forensic issues. In this paper, we explore Signature Identification and Verification using features extracted from pretrained Convolution Neural Network model (Alex Net). All the experiments are performed on signatures from three dataset (SigComp2011) (Dutch, Chinese), SigWiComp2013 (Japanese) and SigWIcomp2015 (Italian). The result shows that features extracted from pretrained Deep Convolution neural network and SVM as classifier show better results than that of Decision Tree. The accuracy of more than 96% for Japanese, Italian, Dutch and Chinese Signatures is obtained with Deep Convolution neural network and SVM as classifier.

Competency for Chinese Handwriting and Signature Examination.

Chinese characters had their root some 4000 years ago and, with the growth in population, are used by over 1.3 billion people worldwide today; it is inevitable that document examiners would encounter cases involving the examination of Chinese handwriting regardless of their location. Although there are extensive studies on the examination of English handwriting in the literature, similar studies on Chinese handwriting are relatively limited. We are seeking to explore the obstacles and challenges for a document examiner who is unfamiliar with Chinese characters to apply the principles of identification and elimination of authorship described in the literature to examine Chinese handwriting, and how can these document examiners demonstrate their competency for conducting Chinese handwriting examination. This study provides a review of the literature, compares the methodology and key features in English and Chinese handwriting examination, and describes various workshops and proficiency testing programs organized to assist document examiners seeking development in Chinese handwriting and signature examination.

Extraction and analysis of the width, gray scale and radian in Chinese signature handwriting

Forensic handwriting examination is a relevant identification process in forensic science. This research obtained ideas from the process of features detection and analysis in forensic handwriting examination. A Chinese signature database was developed and comprised original signatures, freehand imitation forgeries, random forgeries and tracing imitation forgeries. The features of width, gray scale and radian combined with stroke orders were automatically extracted after image processing. A correlation coefficient was used to precisely characterize and express the similarities between signatures. To validate the differences between writers, a multivariate analysis of the variance was employed. The canonical discriminant analysis was performed between the original and non-original signatures; the cross-validation estimated the discriminating power of the width, gray scale and radian data. It is suggested that the extraction and analysis of these properties in Chinese signatures is reasonable. Meanwhile, forensic handwriting examination using the quantitative feature extraction and statistical analysis methods in this research could be performed with a satisfactory result in the discriminant analysis.

Genetic Heterogeneity of Pseudoxanthoma Elasticum: The Chinese Signature Profile of ABCC6 and ENPP1 Mutations

Pseudoxanthoma elasticum (PXE), an autosomal recessive disorder characterized by ectopic mineralization, is caused by mutations in the ABCC6 gene. We examined clinically 29 Chinese PXE patients from unrelated families, so far the largest cohort of Asian PXE patients. In a subset of 22 patients, we sequenced ABCC6 and another candidate gene, ENPP1, followed by pathogenicity analyses for each variant. We identified a total of 17 distinct mutations in ABCC6, 15 of them being previously unreported, including 5 frame-shift and 10 missense variants. In addition, a missense mutation in combination with a recurrent nonsense mutation in ENPP1 was discovered in a pediatric PXE case. No cases with p.R1141X or del23-29 mutations, common in Caucasian patient populations, were identified. The 10 missense mutations in ABCC6 were expressed in mouse liver via hydrodynamic tail-vein injections. One mutant protein showed cytoplasmic accumulation indicating abnormal subcellular trafficking, while the other nine mutants showed correct plasma membrane location. These nine mutations were further investigated for their pathogenicity using a recently developed zebrafish mRNA rescue assay. Minimal rescue of the morpholino-induced phenotype was achieved with 8 of the 9 mutant human ABCC6 mRNAs tested, implying pathogenicity. This study demonstrates that the Chinese PXE population harbors unique ABCC6 mutations. These genetic data have implications for allele-specific therapy currently being developed for PXE.

Quality Aware Online Signature Verification Using Support Vector Machine

A scheme for quality aware online signature verification based on support vector machine was presented and evaluated on the online signature database SVC2004. In this paper, firstly, we conducted experiment on several representative classifiers widely used in online signature verification for comparison. The result showed that DTW and SVM outperformed other classifiers significantly and the EER of SVM was optimistically achieved by 3.67%. Secondly, we divided the signatures into 3 grades with entropy decreasing, that was, incompetent, medium, and excellent according to the quality measure. We found that there were 13 out of 15 Chinese signatures and 16 out of 25 English samples were medium-excellent, which implied that Chinese signature was superior quality and informative. At last, we improved the SVM by adopting soft margin and Gaussian kernel, achieving the EER by 4.18% and 3.67% respectively. Moreover, when quality measure was introduced, the best performance was achieved by 2.48%, which showed 32.43% gain compared with the original Gaussian kernel.

A Comparative Analysis on the Korean and Chinese Electronic Signature System

A Comparative Analysis on the Korean and Chinese Electronic Signature System

Off-line Chinese signature authentication based on integrated neural networks

To assist the signature specialists, the authentication system for off-line Chinese signatures was developed based on a new Integrated Neural Network (INN), which was composed of the modules, namely Feature Distributed Network (FDN), Neutral Networks Classifier (NNC) and Decision Fusional Network (DFN). In the method, a specific classifier was created for each of the signatures,and both training sets were constructed to solve the verification and recognition problems. The proposed method yields satisfactory results on signature verification experiments, and can be used to support human experts to authenticate handwriting signature.

Off-line Chinese signature recognition system based on neural network

This paper tried to combine neural network and signature verification,using the Back Propagation algorithm(BP) of the multi player Neural Network based on matlab neural network box.And a method of feature extraction based on Gabor fliter and Zernike moment was proposed.The experiment shows that the method is more effective and feasible,and recognition rate has been up to 93.70%.

O®-Line Chinese Signature Veri¯cation Based on Segmentation and HMM

Automatic off-line Chinese signature verification is a very complicated problem. The difficulty lies in the fact that it is hard to find a signature model that is insusceptible to intro-classes distance and at the same time is sensitive to inter-classes distance. In this paper, a simple robust segmentation method with low computation cost is proposed which can successfully extract strokes of handwritten Chinese characters and takes into account the characteristics of signature verification. After being segmented and feature extracted, each signature is represented by a series of six-dimensional vectors quantized using an improved vector quantization method to obtain a series of observation values. Twelve genuine samples were used to train the signature DHMM of a writer and 4576 signatures are used in the test. The cross error rate is only 5.5%.

Linear phase FIR differentiator design based on maximum signal-to-noise ratio criterion

In this paper, a new approach to the design of digital FIR differentiators is presented. The differentiator is linear phase and has zero derivatives at DC frequency. The design is based on the maximization of signal-to-noise ratio (SNR) at the output of the differentiator. The optimal filter coefficients are obtained from the generalized eigenvector associated with the maximum eigenvalue of a pair of symmetric matrices. Estimation of the time derivative of polynomial signal, sinusoidal signal and handwritten Chinese signature is used to demonstrate that the proposed method provides better accuracy and higher SNR than the conventional differentiator methods.

Off-line Chinese signature verification based on support vector machines

This paper proposes a novel off-line Chinese signature verification method based on support vector machines. The method uses both static features and dynamic features. The static features include moment features and 16-direction distribution (an improvement on 4-direction distribution). The dynamic features include gray distribution and stroke width distribution. At last, support vector machine is used to classify the signatures. The main steps of constructing a signature verification system are discussed and experiments on real data sets show that the average error rate can reach 5%, which is obviously satisfactory.

Off-Line Verification for Chinese Signatures

Signature verification is one of the important methodologies for personnel identification. Because of its wide applications to security practices quite a number of signature verification techniques associated with various features have been proposed. The features utilized in a signature verification system directly affect the performance of the system. In this paper, we review a variety of recently proposed features of signatures and present two new features. Each of these features is examined to evaluate its performance for Chinese signature verification. Then we choose and combine the features that establish good verification rates. As a result, we propose an off-line Chinese signature verification system based on this combination of features. The experimental results show that the proposed system achieves a good performance in term of verification rate.

Chinese document indexing based on a new partitioned signature file: Model and evaluation

AbstractIn this article we investigate the use of signature files in Chinese information retrieval system and propose a new partitioning method for Chinese signature file based on the characteristic of Chinese words. Our partitioning method, called Partitioned Signature File for Chinese (PSFC), offers faster search efficiency than the traditional single signature file approach. We devise a general scheme for controlling the trade‐off between the false drop and storage overhead while maintaining the search space reduction in PSFC. An analytical study is presented to support the claims of our method. We also propose two new hashing methods for Chinese signature files so that the signature file will be more suitable for dynamic environment while the retrieval performance is maintained. Furthermore, we have implemented PSFC and the new hashing methods, and we evaluated them using a large‐scale real‐world Chinese document corpus, namely, the TREC‐5 (Text REtrieval Conference) Chinese collection. The experimental results confirm the features of PSFC and demonstrate its superiority over the traditional single signature file method.

Wavelet-Based Off-Line Handwritten Signature Verification

In this paper, a wavelet-based off-line handwritten signature verification system is proposed. The proposed system can automatically identify useful and common features which consistently exist within different signatures of the same person and, based on these features, verify whether a signature is a forgery or not. The system starts with a closed-contour tracing algorithm. The curvature data of the traced closed contours are decomposed into multiresolutional signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for matching. Moreover, a statistical measurement is devised to decide systematically which closed contours and their associated frequency data of a writer are most stable and discriminating. Based on these data, the optimal threshold value which controls the accuracy of the feature extraction process is calculated. The proposed approach can be applied to both on-line and off-line signature verification systems. Experimental results show that the average success rates for English signatures and Chinese signatures are 92.57% and 93.68%, respectively.