• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1271-1279
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• 2020

Text region detection is a technology that detects text area in handwriting or printed documents. The detected text areas are digitized through a recognition step, which is used in various fields depending on the purpose of use. However, the detection result of the small text unit is not suitable for the industrial field. In addition, the border of tables in the document that it causes miss-detected results, which has an adverse effect on the recognition step. To solve the issues, we propose a method for detecting text region using the border information of the table. In order to utilize the border information of the table, the proposed method adjusts the flow of two decoders. Experimentally, we show improved performance using the table border pseudo label based on weak supervised learning.

• Smart Media Journal
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• v.12 no.1
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• pp.24-31
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• 2023

Meta-learning is metacognition that instantly distinguishes between knowing and unknown. It is a learning method that adapts and solves new problems by self-learning with a small amount of data.A few-shot learning method is a type of meta-learning method that accurately predicts query data even with a very small support set. In this study, we propose a method to solve the limitations of the prototype created with the mean-point vector of each class. For this purpose, we use the few-shot learning method that created the prototype used in the few-shot learning method as the median prototype. For quantitative evaluation, a handwriting recognition dataset and mini-Imagenet dataset were used and compared with the existing method. Through the experimental results, it was confirmed that the performance was improved compared to the existing method.

• (The) Research of the performance art and culture
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• no.43
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• pp.357-380
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• 2021

Akjang yoram 『樂章要覽』 is a book that summarizes only the important contents from the Akjang 樂章. Akjang 樂章 is arranged in the first half, and score 樂譜 is arranged in the second half. It seems that Akjang yoram 『樂章要覽』 passed through a total of four stages through the time when the handwriting and the lyrics were written. The presence of various handwriting and traces of modifications means that it has been passed through by several people, so it is not unrelated to the fact that several traces remain on the back of the cover of Akjang yoram 『樂章要覽』. The first part of the Akjang 樂章 is a method of presenting the name and lyrics of the accompanying music based on the ritual procedure, and in particular, the lyrics are written in Chinese characters and Hangeul sounds to improve readability. The score in the second half complies with the ritual procedures, but boldly omits overlapping melodies, and is composed based on the music, and various symbols are used to capture the expression of court music. This structure is a reflection of the direction we practiced to harmonize with the music after prior ritual procedures and diction. This was a device to increase the efficiency of music education and music practice for the court musician. The characteristics of the musical pieces are that they consist of essential musical pieces that must be mastered as musicians. In addition, the name Kim Hyung-sik 金亨植 is noted on the back cover of Akjang yoram 『樂章要覽』, and he was a court musician who was active in the age of King Sunjo 純祖. In other words, the musical pieces included in Akjang yoram 『樂章要覽』 are the core repertoire played by court musicians like Kim Hyung-sik 金亨植. Akjang yoram 『樂章要覽』 is a 'music practice booklet' containing the daily life of court musicians. Akjang yoram 『樂章要覽』 is a booklet designed for the purpose of teaching the court musicians to sing while correctly pronouncing the lyrics in major ceremonies. It is even more noteworthy in that Kim Hyung-sik 金亨植 was an owner. In addition to the fact that Kim Hyung-sik's name remains, and in the practicality of being used by various court musicians reflecting and modifying the changes of the times, it is meaningful in that it contains the path of court musicians who spent a lot of time and time to transmit court music.

• The Journal of Korean Academy of Sensory Integration
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• v.8 no.1
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• pp.61-72
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• 2010

Objective : This study tried to identify evaluation tools and intervention approaches that have been used regarding visual motor skills of children, in order to suggest the best evaluation method and intervention strategy. Methods : This study employed a systemic review. Papers researched for the review were selected from the PubMed which is a web engine to search academic articles. For search, time period of publication was limited from January 2001 to Jun 2010, and key words used were "visual motor and occupational therapy", "visuomotor and occupational therapy", and "perception and motor and occupational therapy". 13 papers among total 161 findings were selected for data analysis. Results : Through literature review, followings were founded. For population targeted in studies, children with developmental disorder are majority(20.00%) and those with handwriting problems are another major group(13.33%). DTVP(33.33%) and VMI(26.67%) are most common tools used to evaluate visual motor skills. Most frequently used intervention method is developmental skill based program(58.82) and the second common method is sensory integration therapy and sensory-based intervention(23.53%). Regarding the effectiveness of occupational therapy for visual motor skill, positive evidences with statistical power take 72.73%. Conclusion : The results imply that occupational therapy is effective for visual motor skill in children. It is suggested that further studies are needed to encompass effectiveness of occupational therapy in terms of children's occupational performance.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.