• 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.

• ETRI Journal
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• v.40 no.6
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• pp.774-787
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• 2018

Arabic handwriting segmentation and recognition is an area of research that has not yet been fully understood. Dealing with Arabic ligature segmentation, where the Arabic characters are connected and unconstrained naturally, is one of the fundamental problems when dealing with the Arabic script. Arabic character-recognition techniques consider ligatures as new classes in addition to the classes of the Arabic characters. This paper introduces an enhanced technique for Arabic handwriting recognition using the deep belief network (DBN) and a new morphological algorithm for ligature segmentation. There are two main stages for the implementation of this technique. The first stage involves an enhanced technique of the Sari segmentation algorithm, where a new ligature segmentation algorithm is developed. The second stage involves the Arabic character recognition using DBNs and support vector machines (SVMs). The two stages are tested on the IFN/ENIT and HACDB databases, and the results obtained proved the effectiveness of the proposed algorithm compared with other existing systems.

• Korean Journal of Occupational Therapy
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• v.26 no.4
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• pp.43-55
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• 2018

Objective : The purpose of this study was to investigate the effects of fidget spinner training on the hand function and handwriting legibility of lower grade elementary school studens. Methods : This study randomly assigned a study group of 12 children and control group of 12 children from 24 children in grade 1 and 2 (ages 7 through 8), whose are dominantly right handed. The study used was a pre-post process. The intervention was conducted only on the study group twice a week for 5 weeks and for 40 minutes per session, for a total of ten sessions. The measuring instruments used to compare the hand functions and handwriting legibility were the Jebsen-Taylor Hand Function Test, Grip Strength Test, and Legibility Test. The data analysis used a Wilcoxon signed rank, Mann-Whitney U and Chi-Square cross analysis. Results : The fidget spinner training showed significant improvement in the study group's hand function(grip strength and handwriting legibility) and a significant difference was shown between the control and study groups. Conclusion : This study confirmed the value and utility of a fidget spinner as a tool for improving the hand function and handwriting legibility of elementary school students in lower grades. Future studies are expected to verify the effectiveness of the fidget spinner training based on the present study.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.316-321
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• 2017

We analyze the online Hangul handwriting recognition problem (HHR) and present solutions based on recurrent neural networks. The solutions are organized according to the three kinds of sequence labeling problem - sequence classifications, segment classification, and temporal classification, with additional consideration of the structural constitution of Hangul characters. We present a stacked gated recurrent unit (GRU) based model as the natural HHR solution in the sequence classification level. The proposed model shows 86.2% accuracy for recognizing 2350 Hangul characters and 98.2% accuracy for recognizing the six types of Hangul characters. We show that the type recognizing model successfully follows the type change as strokes are sequentially written. These results show the potential for RNN models to learn high-level structural information from sequential data.

• 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.24 no.5
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• pp.591-599
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• 2020

Deep learning is widely utilized to classify or recognize objects of real-world. An abundance of data is trained on high-performance computers and a trained model is generated, and then the model is loaded in an inferencer. The inferencer is used in various environments, so that it may cause unrecognized objects or low-accuracy objects. To solve this problem, real-world objects are collected and they are trained periodically. However, not only is it difficult to immediately improve the recognition rate, but is not easy to learn an inferencer on embedded devices. We propose a real-time handwriting recognizer based on partial learning on embedded devices. The recognizer provides a training environment which partially learn on embedded devices at every user request, and its trained model is updated in real time. As this can improve intelligence of the recognizer automatically, recognition rate of unrecognized handwriting increases. We experimentally prove that learning and reasoning are possible for 22 numbers and letters on RK3399 devices.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.81-89
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• 2021

This study proposes a text interface for support of language-based educational contents in a mobile platform environment. The proposed interface utilizes deep learning as an input structure to write words through handwriting. Based on GUI (Graphical User Interface) using buttons and menus of mobile platform contents and input methods such as screen touch, click, and drag, we design a text interface that can directly input and process handwriting from the user. It uses the EMNIST (Extended Modified National Institute of Standards and Technology database) dataset and a trained CNN (Convolutional Neural Network) to classify and combine alphabetic texts to complete words. Finally, we conduct experiments to analyze the learning support effect of the interface proposed by directly producing English word education contents and to compare satisfaction. We compared the ability to learn English words presented by users who have experienced the existing keypad-type interface and the proposed handwriting-based text interface in the same educational environment, and we analyzed the overall satisfaction in the process of writing words by manipulating the interface.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.13-22
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• 2024

Currently, with the proliferation of digital devices, the significance of handwritten texts in daily lives is gradually diminishing. As the use of keyboards and touch screens increase, a decline in Korean handwriting quality is being observed across a broad spectrum of Korean documents, from young students to adults. However, Korean handwriting still remains necessary for many documentations, as it retains individual unique features while ensuring readability. To this end, this paper aims to implement an application designed to improve and correct the quality of handwritten Korean script The implemented application utilizes the CRAFT (Character-Region Awareness For Text Detection) model for handwriting area detection and employs the VGG-Feature-Extraction as a deep learning model for learning features of the handwritten script. Simultaneously, the application presents the user's handwritten Korean script's reliability on a syllable-by-syllable basis as a recognition rate and also suggests the most similar fonts among candidate fonts. Furthermore, through various experiments, it can be confirmed that the proposed application provides an excellent recognition rate comparable to conventional commercial character recognition OCR systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1201-1206
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• 2014

Retail stores are mostly relying their product management on handwriting. This handwriting management is limited and poor at managing the status of stocking and releasing. Also, to confirm real-time product status, additional statistic system is required. POS system developed to overcome foresaid problems is operating only in big and special stores because of expensive price and limited space. Therefore, new automated management system which has low installation price needs to be developed and adopted instead of handwriting management system. In this paper, we developed real-time product management system which can manage the products in retail stores by utilizing NFC function in mobile device.

• Journal of Korean Critical Care Nursing
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• v.9 no.1
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• pp.1-14
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• 2016

Purpose: To investigate the post-intensive care syndrome (PICS) and to analyze the factors affecting the quality of life (QoL) of survivors of critical illness. Methods: Subjects were 114 outpatients who had been discharged from intensive care units of a university hospital in B city, Korea. From July 30 through September 30, 2015, PICS was assessed using the Korean Montreal Cognitive Assessment, Hospital Anxiety-Depression Scale, Korean Instrumental/Activities of Daily Living (K-I/ADL) index, and handwriting transformation, while physical and mental health-related QoL was measured using the SF-12. Results: Of the subjects, 39.5% were screened for mild cognitive disorder and 23.7% experienced handwriting transformation after discharge. Multiple regression analysis revealed that restraint application, current job, time of ${\geq}36$ months after discharge, depression, anxiety, and handwriting transformation accounted for 40.9% of the physical health-related QoL, and depression, anxiety and experience of delirium accounted for 62.4% of the mental health-related QoL. Conclusions: It is necessary to make efforts to reduce restraint application in intensive care units and prevent the occurrence of delirium, with the objective of reducing PICS and improving the QoL of critical illness survivors.