• International journal of advanced smart convergence
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• v.9 no.1
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• pp.113-120
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• 2020

Recently, the license plate OCR system has been commercialized in a variety of fields and preferred utilizing low-cost embedded systems using only cameras. This system has a high recognition rate of about 98% or more for the environments such as parking lots where non-vehicle is restricted; however, the environments where non-vehicle objects are not restricted, the recognition rate is about 50% to 70%. This low performance is due to the changes in the environment by non-vehicle objects in real-time situations that occur anomaly data which is similar to the license plates. In this paper, we implement the appropriate anomaly detection based on semi-supervised learning for the license plate OCR system in the real-time environment where the appearance of non-vehicle objects is not restricted. In the experiment, we compare systems which anomaly detection is not implemented in the preceding research with the proposed system in this paper. As a result, the systems which anomaly detection is not implemented had a recognition rate of 77%; however, the systems with the semi-supervised learning based on anomaly detection had 88% of recognition rate. Using the techniques of anomaly detection based on the semi-supervised learning was effective in detecting anomaly data and it was helpful to improve the recognition rate of real-time situations.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.9-17
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• 2009

A face database has contained a large amount of facial information data since face recognition was widely used. With the increase of facial information data, the face recognition based on distributed processing method has been noticed as a major topic. In existing studies, there were lack of discussion about the transferring method for large data. So, we proposed a fuzzy logic-based bit compression rate selection method for distributed face recognition. The proposed method selects an effective bit compression rate by fuzzy inference based on face recognition rate, processing time for recognition, and transferred bit length. And, we compared the facial recognition rate and the recognition time of the proposed method to those of facial information data with no compression and fixed bit compression rates. Experimental results demonstrates that the proposed method can reduce processing time for face recognition with a reasonable recognition rate.

• Journal of Fashion Business
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• v.21 no.3
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• pp.14-28
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• 2017

This purpose of this study to analyze effect material properties have on change in QR code recognition rate according to change of materials by comparing recognition rate of color QR code. QR code applied to textile materials has the advantage of being washable and being applicable to lost child prevention goods or clothes or a person with dementia through record of information relating to the material or input of additional information, differently from QR code printed on the conventional paper. An effective method of entering QR code in textile materials is Digital Textile Printing(DTP), that facilitates printing by rapidly applying diverse information, and small quantity production. It is possible to tailor various QR codes according to use. Regarding samples to use, cotton material used in clothing products and Tyvek material recently applied to clothing and related products were selected. Reactive dyes were used for cotton, pigment was used for Tyvek, and QR code was printed with an inkjet printer by direct printing method. Printing methods and surface textures are different between cotton and Tyvek. It was revealed that consequent print results and results of recognition rate were different. Regarding color to be printed, 2015 S/S - 2017 S/S color presented by Pantone was used. Color combination affected recognition rate of color QR code. Understanding color combination, material properties and print characteristics may be helpful in increasing recognition rate of color QR code, and may contribute to usability of color QR code applied to textile materials in the future.

• Journal of Fashion Business
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• v.20 no.4
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• pp.50-71
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• 2016

This study analyzed the recognition rate of QR codes printed by digital transfer textile printing for mobile application. The purpose was to identify conditions that increase recognition rates of QR codes printed on textile, in order to increase utility of QR codes in the textile and fashion industries. The study focused on differences in the color of the QR codes and denier, which is a unit of textile fineness measurement, of the textile on which the QR codes were printed. And the recognition rates of QR codes according to the color and denier were analyzed. According to the result of this study, the colors of QR codes had an effect on the recognition of the codes by mobile applications. Specifically, strong contrast, i.e., bright background and relatively dark module, increased the recognition rate of the QR codes. Digital transfer textile printing tend to change the hue of red and yellow and increase brightness, and change in the printed colors also had an effect on the recognition rate of QR codes. There was a clear difference in the color and recognition rate of the printed QR code according to the denier of the textile, and this suggests denier has an effect on the recognition rate. The findings in this study can provide basic data for future research on QR codes digital printed on textile.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.27-36
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• 2016

Recognition of grapheme is a very important process in the recognition within 'Hangul(Korean written language)' letters using phoneme recognition. It is because the success or failure in the recognition of phoneme greatly affects the recognition of letters. For this reason, it is reported that separation of phonemes is the biggest difficulty in the phoneme recognition study. The current study separates and suggests the new phonemes that used the connective elements that are helpful for dividing phonemes, recommends the features for recognition of such suggested phonemes, databases this, and carried out a set of experiments of recognizing phonemes using the suggested features. The current study used 350 letters in the experiment of phoneme separation and recognition. In this particular kind of letters, there were 1,125 phonemes suggested. In the phoneme separation experiment, the phonemes were divided in the rate of 100%, and the phoneme recognition experiment showed the recognition rate of 98% in recognizing only 14 phonemes into different ones.

• Speech Sciences
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• v.8 no.4
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• pp.17-33
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• 2001

In this paper, we implement the large variable vocabulary speech recognition system, which is characterized by no additional pre-training process and no limitation of recognized word list. We have designed the system in order to achieve the high recognition rate using the decision tree based state tying algorithm and in order to reduce the processing time using the gaussian selection based variable flooring algorithm, the limitation algorithm of the number of nodes and ENNS algorithm. The gaussian selection based variable flooring algorithm shows that it can reduce the total processing time by more than half of the recognition time, but it brings about the reduction of recognition rate. In other words, there is a trade off between the recognition rate and the processing time. The limitation algorithm of the number of nodes shows the best performance when the number of gaussian mixtures is a three. Both of the off-line and on-line experiments show the same performance. In our experiments, there are some differences of the recognition rate and the average recognition time according to the distinction of genders, speakers, and the number of vocabulary.

• Phonetics and Speech Sciences
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• v.9 no.4
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• pp.43-50
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• 2017

These days voice models of speech recognition software are sophisticated enough to process the natural speech of people without any previous training. However, not much research has reported on the use of speech recognition tools in the field of pronunciation education. This paper examined Google speech recognition of a short English paragraph produced by Korean college students in clear and casual speech styles in order to diagnose and resolve students' pronunciation problems. Thirty three Korean college students participated in the recording of the English paragraph. The Google soundwriter was employed to collect data on the word recognition rates of the paragraph. Results showed that the total word recognition rate was 73% with a standard deviation of 11.5%. The word recognition rate of clear speech was around 77.3% while that of casual speech amounted to 68.7%. The reasons for the low recognition rate of casual speech were attributed to both individual pronunciation errors and the software itself as shown in its fricative recognition. Various distributions of unrecognized words were observed depending on each participant and proficiency groups. From the results, the author concludes that the speech recognition software is useful to diagnose each individual or group's pronunciation problems. Further studies on progressive improvements of learners' erroneous pronunciations would be desirable.

• ETRI Journal
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• v.46 no.1
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• pp.22-34
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• 2024

Exposure to varied noisy environments impairs the recognition performance of artificial intelligence-based speech recognition technologies. Degraded-performance services can be utilized as limited systems that assure good performance in certain environments, but impair the general quality of speech recognition services. This study introduces an audiovisual speech recognition (AVSR) model robust to various noise settings, mimicking human dialogue recognition elements. The model converts word embeddings and log-Mel spectrograms into feature vectors for audio recognition. A dense spatial-temporal convolutional neural network model extracts features from log-Mel spectrograms, transformed for visual-based recognition. This approach exhibits improved aural and visual recognition capabilities. We assess the signal-to-noise ratio in nine synthesized noise environments, with the proposed model exhibiting lower average error rates. The error rate for the AVSR model using a three-feature multi-fusion method is 1.711%, compared to the general 3.939% rate. This model is applicable in noise-affected environments owing to its enhanced stability and recognition rate.

• Journal of International Society for Simulation Surgery
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• v.2 no.2
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• pp.64-66
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• 2015

Purpose : In this paper we proposed cascade feature filter and projection method for rapid human face recognition for the large-scale high-dimensional face database. Materials and Methods : The relevant features are selected from the large feature set using Fast Correlation-Based Filter method. After feature selection, project them into discriminant using Principal Component Analysis or Linear Discriminant Analysis. Their cascade method reduces the time-complexity without significant degradation of the performance. Results : In our experiments, the ORL database and the extended Yale face database b were used for evaluation. On the ORL database, the processing time was approximately 30-times faster than typical approach with recognition rate 94.22% and on the extended Yale face database b, the processing time was approximately 300-times faster than typical approach with recognition rate 98.74 %. Conclusion : The recognition rate and time-complexity of the proposed method is suitable for real-time face recognition system on the large-scale high-dimensional face database.

• ETRI Journal
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• v.36 no.6
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• pp.1016-1022
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• 2014

This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers.