• Journal of Information Processing Systems
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• v.13 no.4
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• pp.893-913
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• 2017

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

• Analytical Science and Technology
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• v.23 no.2
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• pp.205-215
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• 2010

The purpose of this study is to examine Hangul signature changes of writers under the influence of alcohol using Scale for Handwriting Identification (SHWI). It has been recognized that handwriting is influenced by alcohol. However, in Korea, there has been no study which examined the handwriting changes after drinking alcohol. This study confirmed the differences between signatures in sobriety at police station (SIS) and signatures under the influence of alcohol at sobriety checkpoint (S-UIA) by analyzing the Hangul signature of 30 persons. The comparative characteristics are size, space, omission, writing order, connection. The changes of more than one characteristic were observed among the 27 out of 30 persons. Three of 30 persons did not show any change between S-IS and S-UIA.

• 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 Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.21-26
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• 2013

The objective of this study was to develop a system for handwriting recognition in three dimensions (3D) to authenticate users. While previous studies have used a stylus pen for two-dimensional input on a tablet, this study uses the Wii Remote controller because it can capture 3D human motion and could therefore be more effective means of recognition. The information obtained from a Wii Remote controller included x and y coordinates, acceleration (x, y, z), angular velocity (pitch, yaw, roll), twelve input buttons, and time. The proposed system calculates distances using six features extracted after preprocessing the data. In an experiment where 15 subjects wrote "AIZU" 10 times, we obtained a 94.8% identification rate using a combination of writing velocity, the peak value of pitch, and the peak value of yaw. This suggests that this system holds promise for handwriting-based authentication in the future.

• Analytical Science and Technology
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• v.23 no.6
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• pp.607-614
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• 2010

Handwriting identifications are often faced with difficulties in evaluating handwriting by persons under the influence of alcohol. Cerebellar dysfunction is associated with deficits in the control of movement extent, as well as changes in the amplitude and relative amounts of acceleration and deceleration and action tremor. Although numerous articles are available on the subject of alcohol influence on handwriting quality, most of them were based on empirical data such as experience method, without any statistical evaluation. In this study, Hangul signature giyeok and nieun of consonant were measured in samples of handwriting. The result of this study can be used as a basic data of comparison on handwriting by identifying consistency of features and relative individualization of.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.63-68
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• 2010

Recently, to accelerate the Ubiquitous generation, the input interface of the mobile machinery and tools are actively being researched. In addition with the existing interfaces such as the keyboard and curser (mouse), other subdivisions including the handwriting, voice, vision, and touch are under research for new interfaces. Especially in the case of small-sized mobile machinery and tools, there is a increasing need for an efficient input interface despite the small screens. This is because, additional installment of other devices are strictly limited due to its size. Previous studies on handwriting recognition have generally been based on either two-dimensional images or algorithms which identify handwritten data inserted through vectors. Futhermore, previous studies have only focused on how to enhance the accuracy of the handwriting recognition algorithms. However, a problem arisen is that when an actual handwriting is inserted, the user must select the classification of their characters (e.g Upper or lower case English, Hangul - Korean alphabet, numbers). To solve the given problem, the current study presents a system which distinguishes different languages by analyzing the form/shape of inserted handwritten characters. The proposed technique has treated the handwritten data as sets of vector units. By analyzing the correlation and directivity of each vector units, a more efficient language distinguishing system has been made possible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.158-165
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• 2019

Although evaluation of writing disabilities identification and timely intervention are required, clinicians adopt a manual scoring method and there is a possibility of error due to subjective evaluation. In this study, the size ratio and position of letters are digitized and quantified through image processing of offline handwritten characters. We tried to evaluate objectively and accurately the performance of writing through comparison with existing methods. From November 12th to 16th, 2018, 20 adults without neurological injury were selected. They used a pencil to follow the 10 words, 2 sentence stimuli after keeping the usual habit, and we collected the writing test data. The results showed that the height of the word was 1.2 times larger than the width and it tilted to the lower left. The spacing interval was 9mm on average. In the Paired T test, a high correlation was showed between our system and existing methods in the word and sentence 2. This demonstrated the possibility as a testing tool. This study evaluated objectively and precisely writing performance of offline handwritten characters through image processing and provided preliminary data for performance standards. In the future, it can be suggested as a basic data on writing diagnosis of various ages.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1114-1135
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• 2018

A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual super-classifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.

• International Journal of Contents
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• v.13 no.3
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• pp.38-42
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• 2017

Documents of the last few decades typically include more than one kind of language, so linguistic classification of each word is essential, especially in terms of English and Korean in handwritten documents. Traditional methods mostly use conventional features of structural or stroke features, but sometimes they fail to identify many characteristics of words because of complexity introduced by handwriting. Therefore, traditional methods lead to a considerably more-complicated task and naturally lead to possibly poor results. In this study, convolutional neural network (CNN) is used for classification of English and Korean handwritten words in text documents. Experimental results reveal that the proposed method works effectively compared to previous methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.151-153
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• 2016

최근 스마트폰, PC, 태블릿 같은 전자기기들이 발전하면서 기계를 통해 소통하는 시대가 왔다. 기계와 소통하기 위해 우리가 사용하는 문자를 인식하는 것은 중요한 일이다. 이런 전자기기들이 문자, 영상인식을 해야 할 필요성이 더욱 증가함에 따라 머신러닝의 중요성이 대두되었다. 머신러닝은 컴퓨터의 학습을 위해 알고리즘과 기술을 개발하는 분야를 말한다. 머신러닝의 기법과 관련된 알고리즘의 종류는 수없이 많다. 그 중에서도 Neural Network는 사람의 뇌 신경구조를 토대로 착안하여 네트워크를 만들고 이를 학습에 이용한 머신러닝 기법이다. 이런 인공지능 알고리즘인 Neural Network 구조를 바탕으로 특징을 추출하여 학습을 하는 Convolution Neural Network 기법의 사용이 늘고 있다. 본 논문에서는 Neural Network와 Convolution Neural Network의 알고리즘을 이용한 필기체 인식 실험을 하고 그 내용을 비교하였다.