• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.25-32
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• 2009

In this paper, we propose a new handwriting verification method using pattern analysis in off-line environments. The proposed method first segments character regions in a document and extracts effective features from the segmented regions. It then estimates the similarity between the extracted non-linear features and reference ones by using dynamic time warping and principal component analysis. Our handwriting verification method extracts handwriting features effectively and enables the verification of handwriting with various lengths of features as well as ones of short patterns. The experimental results show that our method outperforms others in terms as accuracy. We expect that the proposed method will automate the manual handwriting verification tasks and provide much objectivity on handwriting identification.

• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.147-154
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• 2005

In conventional approaches such as a functinal approach or a parametric approach to online signature verification, which could not deal with the local shape of signature, much various important informations inherent in the local part of signature shape have been overlooked. In this paper, we try a structural approach in which a signature is represented as a structural form of handwriting primitives and the local parts along a signature handwriting can be selectively compared according to their discrimination power in the process of signature verification, As a result, the error rate is diminished in the case that the weights of subpattern units is applied into comparing process, which is the degree of discrimination power of local part. And also, the global variation and complexity of each signature extracted from the analysis of local shape is found useful in determining the decision threshold more precisely.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.25-37
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• 2019

Inspired by the rapid development of deep learning and online biometrics-based authentication, we propose a handwritten one-time password authentication system which employs deep learning-based handwriting recognition and writer verification techniques. We design a convolutional neural network to recognize handwritten digits and a Siamese network to compute the similarity between the input handwriting and the genuine user's handwriting. We propose the first application of the second edition of NIST Special Database 19 for a writer verification task. Our system achieves 98.58% accuracy in the handwriting recognition task, and about 93% accuracy in the writer verification task based on four input images. We believe the proposed handwriting-based biometric technique has potential for use in a variety of online authentication services under the FIDO framework.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1009-1015
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• 2009

Handwriting verification is a technique of distinguishing the same person's handwriting specimen from imitations with any two or more texts using one's handwriting individuality. This paper suggests an effective verification method for the handwritten signature or text on the off-line environment using pattern recognition technology. The core processes of the method which has been researched in this paper are extraction of letter area, extraction of features employing structural characteristics of handwritten text, feature analysis employing DTW(Dynamic Time Warping) algorithm and PCA(Principal Component Analysis). The experimental results show a superior performance of the suggested method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1435-1441
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• 2018

Online signature verification is one of the simple and efficient methods of identity verification and has less resistance than other biometric technologies. To handle signature verification as a classification problem, it is necessary to gather forgery signatures, which is not easy in most practical applications. It is not easy to obtain a large number of genuine signatures either. In this paper, one class SVM is used to tackle the forgery signature problem and someone else's signatures are used as general handwriting data to solve the genuine signature problem. Someone else's signature does not share shape-based features with the signature to be verified, but it contains the general characteristics of a signature and useful in verification. Verification rate can be improved by using the general handwriting data, which can be confirmed through the experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2298-2304
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• 2017

Online signature verification is one of the simple and efficient method of identity verification and has less resistance than other biometric technologies. In training to build a verification model, negative samples are required to build the model, but in most practical applications it is not easy to get negative samples - forgery signatures. In this paper, proposed is a method using someone else's signatures as negative samples. In verification, shape-based features extracted from the time-sequenced signature data are extracted and a support vector machine is used to verify. SVM tries to map a feature vector to a high dimensional space and to draw a linear boundary in the high dimensional space. SVM is one of the best classifiers and has been applied to various applications. Using general handwriting data, i.e., someone else's signatures which have little in common with positive samples improved the verification rate experimentally, which means that signature verification without negative samples is possible.

• Journal of Computing Science and Engineering
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• v.10 no.2
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• pp.39-50
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• 2016

In this paper, we present a comparative study of spatial domain features for writer identification and verification with different ink width conditions. The existing methods give high error rates, when comparing two handwritten images with different pen types. To the best of our knowledge, we are the first to design the feature with different ink width conditions. To address this problem, contour based features were extracted using a chain code method. To improve accuracy at higher levels, we considered histograms of chain code and variance in bins of histogram of chain code as features to discriminate handwriting samples. The system was trained and tested for 1,000 writers with two samples using different writing instruments. The feature performance is tested on our newly created dataset of 4,000 samples. The experimental results show that the histogram of chain code feature is good compared to other methods with false acceptance rate of 11.67%, false rejection rate of 36.70%, average error rates of 24.18%, and average verification accuracy of 75.89% on our new dataset. We also studied the effect of amount of text and dataset size on verification accuracy.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.325-341
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• 2015

This paper describes the Tezpur University dataset of online handwritten Assamese characters. The online data acquisition process involves the capturing of data as the text is written on a digitizer with an electronic pen. A sensor picks up the pen-tip movements, as well as pen-up/pen-down switching. The dataset contains 8,235 isolated online handwritten Assamese characters. Preliminary results on the classification of online handwritten Assamese characters using the above dataset are presented in this paper. The use of the support vector machine classifier and the classification accuracy for three different feature vectors are explored in our research.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.890-895
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• 2000

본 논문에서는 인공신경망을 이용한 자모 검증을 HMM 기반 온라인 한글인식 시스템에 적용하는 방법론을 제시한다. 본 시스템에서 각각의 자모는 한 개의 HMM 모델과 한 개의 인공신경망 검증기를 갖는다. 자모 검증기는 HMM 네트웍이 생성한 자모 후보 가정을 입력으로 받은 후, 이 가정의 타당성에 대한 사후 확률을 출력한다. 이 사후 확률은 Viterbi 탐색시 탐색 경로에 반영된다. 기존 HMM 시스템의 국소적 특징의 한계를 보완하기 위하여, 한글 자모의 기본획 분석에서 얻어진 구조적, 전역적 특징이 자모 검증기에 사용되었다. 한글 낱자인식에 대한 실험 결과 HMM 기반 인식기에 자모 검증기를 도입함으로서 38.5%의 인식 오류를 줄일 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.540-543
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• 2020

본 논문에서는 대표적인 이미지 분류 모델인 CNN(Convolutional Neural Network)과 시간에 따른 이미지의 변화를 학습할 수 있는 LSTM(Long Short-Term Memory) 기반의 온라인 서명인식 모델을 제안한다. 실제로는 위조서명을 미리 구하기 어렵다는 사실을 고려해 서명검증 대상자가 아닌 타인의 진서명과 대상자의 일반 필기 데이터를 음의 데이터로서 학습에 사용하였다. 실험 결과, 전체 이미지 중 서명 부분의 비율에 따라 좋은 성능을 보이는 검증 모델이 다르며 Accuracy 성능지표를 통해 이 비율이 높거나 낮을 경우 CNN-LSTM 이, 중간일 경우 CNN 이 적합하다는 것을 확인하였다.