• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1273-1280
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• 1996

In order perform high accuracy recognition of text recognition systems, the recognized text must be processed through a post-processing stage using contextual information. We present a system that combines multiple knowledge sources to post-process the output of an optical character recognition(OCR) system. The multiple knowledge sources include characteristics of word, wrongly recognized types of Hangul characters, and Hangul word learning In this paper, the wrongly recognized characters which are made by OCR systems are collected and analyzed. We imput a Korean dictionary with approximately 15 0,000 words, and Korean language texts of Korean elementary/middle/high school. We found that only 10.7% words in Korean language texts of Korean elementary/middle /high school were used in a Korean dictionary. And we classified error types of Korean character recognition with OCR systems. For Hangul word learning, we utilized indexes of texts. With these multiple knowledge sources, we could predict a proper word in large candidate words.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.852-858
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• 1991

An adaptive hierarchical classifier(AHCL) for Korean character recognition using a neural net is designed. This classifier has two neural nets: USACL (Unsupervised Adaptive Classifier) and SACL (Supervised Adaptive Classifier). USACL has the input layer and the output layer. The input layer and the output layer are fully connected. The nodes in the output layer are generated by the unsupervised and nearest neighbor learning rule during learning. SACL has the input layer, the hidden layer and the output layer. The input layer and the hidden layer arefully connected, and the hidden layer and the output layer are partially connected. The nodes in the SACL are generated by the supervised and nearest neighbor learning rule during learning. USACL has pre-attentive effect, which perform partial search instead of full search during SACL classification to enhance processing speed. The input of USACL and SACL is a directional edge feature with a directional receptive field. In order to test the performance of the AHCL, various multi-font printed Hangul characters are used in learning and testing, and its processing its speed and and classification rate are compared with the conventional LVQ(Learning Vector Quantizer) which has the nearest neighbor learning rule.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.953-964
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• 2020

Ever since media contents took over the world, the importance of typography has increased, and the influence of fonts has be n recognized. Nevertheless, the current Hangul font system is very poor and is provided passively, so it is practically impossible to understand and utilize all the shape characteristics of more than six thousand Hangul fonts. In this paper, the characteristics of Hangul font shapes were selected based on the Hangul structure of similar fonts. The stroke element detection training was performed by fine tuning Faster R-CNN Inception v2, one of the deep learning object detection models. We also propose a system that automatically extracts the stroke element characteristics from characters by introducing an automatic extraction algorithm. In comparison to the previous research which showed poor accuracy while using SVM(Support Vector Machine) and Sliding Window Algorithm, the proposed system in this paper has shown the result of 10 % accuracy to properly detect and extract stroke elements from various fonts. In conclusion, if the stroke element characteristics based on the Hangul structural information extracted through the system are used for similar classification, problems such as copyright will be solved in an era when typography's competitiveness becomes stronger, and an automated process will be provided to users for more convenience.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.693-695
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• 2018

In this paper, we propose the improvement of Hangul OCR accuracy through deep learning. OCR is a program that senses printed and handwritten characters in an optical way and encodes them digitally. In the case of the most commonly used Tesseract OCR, the accuracy of English recognition is high. However, Hangul has lower accuracy because it has less learning data for a complex structure. Therefore, in this study, we propose a method to improve the accuracy of Hangul OCR by extracting the character region from the desired image through image processing and using deep learning using it as learning data. It is expected that OCR, which has been developed only by existing alphanumeric and several languages, can be applied to various languages.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1361-1371
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• 2020

Fonts have developed as a visual element, and their influence has rapidly increased around the world. Research on font automation is actively being conducted mainly in English because Hangul is a combination character and the structure is complicated. In the previous study to solve this problem, the stroke element of the character was automatically extracted by applying the object detection by component. However, the previous research was only for similarity, so it was tested on various print style fonts, but it has not been tested on other characters. In order to extract the stroke elements of all characters and fonts, we performed a performance analysis experiment according to the expansion character in the Hangul stroke element extraction training. The results were all high overall. In particular, in the font expansion type, the extraction success rate was high regardless of having done the training or not. In the character expansion type, the extraction success rate of trained characters was slightly higher than that of untrained characters. In conclusion, for the perfect Hangul stroke element extraction model, we will introduce Semi-Supervised Learning to increase the number of data and strengthen it.

• Journal of KIISE
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• v.43 no.11
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• pp.1307-1314
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• 2016

Virtual Hangul keyboards like Chun-Ji-In, Narat-Gul, and QWERTY are based on eyesight recognition, in which input letter positions are fixed in the smartphone environment. The input method of a fixed-position style is not very convenient for visually impaired individuals. In order to resolve the issue of inconvenience of the Hangul input system, we propose a new paradigm of the finger-touch based Hangul input system that does not need eyesight recognition of input buttons. For the convenience of learning the touch-motion based keyboard, finger touches are designed by considering the shape and frequencies of Hangul vowels and consonants together with the preference of fingers. The base position is decided by the first touch of the screen, and the finger-touch keyboard is used in the same way for all the other touch-style devices, regardless of the differences in size and operation system. In this input method, unique finger-touch motions are assigned for Hangul letters that significantly reduce the input errors.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.473-482
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• 2021

Although several Hangul generation models using deep learning have been introduced, they require a lot of data, have a complex structure, requires considerable time and resources, and often fail in style conversion. This paper proposes a model CKFont using the components of the initial, middle, and final components of Hangul as a way to compensate for these problems. The CKFont model is an end-to-end Hangul generation model based on GAN, and it can generate all Hangul in various styles with 28 characters and components of first, middle, and final components of Hangul characters. By acquiring local style information from components, the information is more accurate than global information acquisition, and the result of style conversion improves as it can reduce information loss. This is a model that uses the minimum number of characters among known models, and it is an efficient model that reduces style conversion failures, has a concise structure, and saves time and resources. The concept using components can be used for various image transformations and compositing as well as transformations of other languages.

• 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.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.447-458
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• 2012

Korean wave which started from Korean drama is continuing its popularity with K-pop in China. This positive effect has lead to increases in Korean product export to China, increase in number of Chinese visitors to Korea and increase in number of Chinese population learning Hangul. In this research, empirical study was conducted to analyze the influence of Korean wave contents (drama, movie, K-pop, games) on Chinese population (their intention to purchase Korean product, visit Korea and learn Hangul.) As the result, it is understood that the most influential Korean wave content on Chinese population's intention to purchase Korean cosmetic and clothing products is drama; it is analyzed that K-pop has noticeable influence as well. Korean drama has the greatest influence on Chinese population's intention to visit Korea, purchase cosmetic or plastic surgery tour package and purchase Korean food. K-pop is analyzed as the second most influential factor among Korean wave contents in this category. Korean wave contents which have the most influence on intention to learn Hangul are Korean drama and K-pop, and it is analyzed that K-pop has greater influence than Hangul in this field.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.468-472
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• 1989

In this paper a Korean authoring system is designed to write a CAI courseware in Hangul/English by an author who is a nonprogrammer. It saves nock time in authoring a courseware and maintains high level transplantity among CAI systems. By interfacing ah expert graphic utility, image information can be processed more easily and efficiently. Programming control of the flow of CAI courseware can be ramification and individual learning possible, fitting various demands of learners and learning and learning ability.