• International Journal of Contents
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• v.16 no.1
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• pp.57-64
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• 2020

We sought to compare upper extremity muscle activity between handwriting on paper and touchscreen with dominant and non-dominant hands in younger adults (age 23.90±1.12) and the elderly (age 75.55±5.76). Muscle activity (percent of maximum voluntary contraction) in the biceps brachii muscle, triceps brachii muscle, flexor carpi ulnaris muscle, and extensor carpi ulnaris muscle was measured using an electromyography device. As a result, our data indicate that muscle activity is lower in younger adults than the elderly. Besides, muscle activity is lower in the dominant versus non-dominant hand, and lower when writing using a touchscreen than on paper. These results can be used to support recommending touchscreens in the elderly. Also, they can be used as baseline data for comparing the performance of non-paretic side and paretic side in patients relative to the central nervous system.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.15-17
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• 2021

Handwriting recognition research is mainly focused on deep learning techniques and has achieved a great performance in the last few years. Especially, handwritten Thai digit recognition has been an important research area including generic digital numerical information, such as Thai official government documents and receipts. However, it becomes also a challenging task for a long time. For resolving the unavailability of a large Thai digit dataset, this paper constructs our dataset and learns them with some variants of the CNN model; Decision tree, K-nearest neighbors, Alexnet, LaNet-5, and VGG (11,13,16,19). The experimental results using the accuracy metric show the maximum accuracy of 98.29% when using VGG 13 with batch normalization.

• ETRI Journal
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• v.46 no.4
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• pp.648-659
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• 2024

Author verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.

• Korean Journal of Heritage: History & Science
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• v.46 no.2
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• pp.150-165
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• 2013

This study aims to investigate the original position, the writer of signboard handwriting and the period of Wibongmun and Joyangru in order to restore Wibongmun and Joyangru which have been designated as tangible cultural properties (有形文化財). They also have to be moved in the Gangwon Provincial Office. Wibongmun and Joyangru were established as government offices in chuncheon(春川官衙) and they were used as attached buildings in Chunceon (春川離宮) in 1890. Wibongmun was moved to Gangwon Provincial Office 5 times and Joyangru was moved twice. In order to move them back to the original place, by using the topographic map made by the Japanese Government-General in Korea, we find out Joyangru was located in the exit of Gangwon Provincial Office and greenhouse and we also figure out Wibongmun was located in the garden. While we study historical evidence on handwriting, we also find out the handwriting of Joyangmun was written by Songhaong (松下翁) Jo, Yun-Hyeong (曺允亨). Especially, Joyangru had played a role as a government office and it may be called 'Joyangru' after reconstructing 'Joyangmun' when attached buildings were established. Through this study, we found that the first period and reason of establishing Wibongmun and Joyangru was at least before 1788. Through this study, we can find the period of both and its historic meaning more clearly.

• ETRI Journal
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• v.37 no.1
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• pp.138-146
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• 2015

This paper presents a new non-constraining online optical handwritten signature reconstruction system that, in the main, makes use of a transparent glass pad placed in front of a color camera. The reconstruction approach allows efficient exploitation of hand activity during a signing process; thus, the system as a whole can be seen as a viable alternative to other similar acquisition tools. This proposed system allows people with physical or emotional problems to carry out their own signatures without having to use a pen or sophisticated acquisition system. Moreover, the developed reconstruction signature algorithms have low computational complexity and are therefore well suited for a hardware implementation on a dedicated smart system.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06b
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• pp.275-280
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• 2008

본 논문에서는 한글 글립(glyph)의 형태학적 분석을 통해 자동으로 획을 분할하는 방법을 제안한다. 제안된 방법은 thinning된 한글 글립의 골격(skeleton) 이미지를 기반으로, 획 분리, 획 병합, 그리고 획 볼륨 복원의 세가지 단계를 거쳐 한글의 기본 획들을 추출해 낸다. 실험 결과, 트루타입폰트(TrueType Font)에 대해서는 80%, 손글씨(Handwriting) 글립에 대해서는 72%의 획 분할 정확도를 보였다. 본 논문에서 제안한 방법으로 획득된 획 정보를 이용하여, 향후 한글 손글씨 생성을 위한 연구를 하고자 한다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.166-177
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• 1994

In this paper, an implemenation of on-line continuous recognizer for cursive Hangul handwriting is explained. For the Hangul recognition system, we propose a high speed string matching. The editing process in our proposed string matching is accomplished by single editing path. And the matching results are stored in a heap structure and we decide the user comfortibility of unceasing writing during recognition owing to the high speed matching. In the experimental result, a recongition rate of 86.36% at 1.75 second/character over 21,076 characters collected from 50 persons are abtained. And it is shown that the proposed recognition system is operated properly for the on-line recognition for cursive handwring between graphemes.

• 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.

• International Journal of Contents
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• v.11 no.3
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• pp.7-13
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• 2015

One of the biggest problems of skeletonization is the occurrence of distortions at the junction point of the final binary image. At the junction area, a single point usually becomes a small stroke, and the corresponding trajectory task, as well as the OCR, consequently becomes more complicated. We therefore propose an adaptive post-processing method that uses an adaptive threshold technique to correct the distortions. Our proposed method transforms the distorted segments into a single point so that they are as similar to the original image as possible, and this improves the static handwriting images after the skeletonization process. Further, we attained promising results regarding the usage of the enhanced skeletonized images in other applications, thereby proving the expediency and efficiency of the proposed method.