• Journal of IKEEE
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• v.25 no.1
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• pp.25-31
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• 2021

The conventional credit card number recognition system generally needs a card to be placed in a designated location before its processing, which is not an ideal user experience especially for people with visual impairment. To improve the user experience, this paper proposes a novel algorithm that can automatically detect the location of a credit card number based on the fact that a group of sixteen digits has a fixed aspect ratio. The proposed algorithm first performs morphological operations to obtain multiple candidates of the credit card number with >4:1 aspect ratio, then recognizes the card number by testing each candidate via OCR and BIN matching techniques. Implemented with OpenCV and Firebase ML, the proposed scheme achieves 77.75% accuracy in the credit card number recognition task.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.129-131
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• 2006

The performances of neural network systems mainly depend on the kind and the number of input patterns for its training. Hence, the kind of input patterns as well as its number is very important for the character recognition system using back-propagation network. The more input patters are used, the better the system recognizes various characters. However, training is not always successful as the number of input patters increases. Moreover, there exists a limit to consider many input patterns of the recognition system for cursive script characters. In this paper we present a new character recognition system using the back-propagation neural networks. By using an additional neural network, an input pattern generation method is provided for increasing the recognition ratio and a successful training. We firstly introduce the structure of the proposed system. Then, the character recognition system is investigated through some experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.49-54
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• 2006

The recent fingerprint recognition system has unstable factors, such as copy of fingerprint patterns and hacking of fingerprint feature point, which mali cause significant system error. Thus, in this research, we used the fingerprint as the main recognition device and then implemented the multi-biometric recognition system in serial using the speech recognition which has been widely used recently. As a multi-biometric recognition system, once the speech is successfully recognized, the fingerprint recognition process is run. In addition, speaker-dependent DTW(Dynamic Time Warping) algorithm is used among existing speech recognition algorithms (VQ, DTW, HMM, NN) for effective real-time process while KSOM (Kohonen Self-Organizing feature Map) algorithm, which is the artificial intelligence method, is applied for the fingerprint recognition system because of its calculation amount. The experiment of multi-biometric recognition system implemented in this research showed 2 to $7\%$ lower FRR (False Rejection Ratio) than single recognition systems using each fingerprints or voice, but zero FAR (False Acceptance Ratio), which is the most important factor in the recognition system. Moreover, there is almost no difference in the recognition time(average 1.5 seconds) comparing with other existing single biometric recognition systems; therefore, it is proved that the multi-biometric recognition system implemented is more efficient security system than single recognition systems based on various experiments.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.327-332
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• 2016

This study is a leading research project to develop an automatic grade decision making algorithm of a 6-years-old fresh ginseng. For this work, we developed a Ginseng image acquiring instrument which can take 4-direction's images of a Ginseng at the same time and obtained 245 jingen images using the instrument. The 12 parameters were extracted for each image by a manual way. Lastly, 4 parameters were selected depending on a Ginseng grade classification criteria of KGC Ginseng research institute and a survey result which a distribution of averaging 12 parameters. A pattern recognition classifier was used as a support vector machine, designed to "k-class classifier" using the OpenCV library which is a open-source platform. We had been surveyed the algorithm performance(Correct Matching Ratio, False Acceptance Ratio, False Reject Ratio) when the training data number was controlled 10 to 20. The result of the correct matching ratio is 94% of the $1^{st}$ ginseng grade, 98% of the $2^{nd}$ ginseng grade, 90% of the $3^{rd}$ ginseng grade, overall, showed high recognition performance with all grades when the number of training data are 10.

• The Korean Journal of Community Living Science
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• v.21 no.4
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• pp.581-593
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• 2010

This study was performed to find out the correct recognition and stabilization of the food labeling system, and the dietary behavior of college students for processed food as well as their recognition of the food labeling system as observed according to their residence type. A questionnaire was composed with three divisions containing general items, the dietary behavior for processed food and the effectiveness of the nutrition labeling system. The data was analyzed by SPSS WIN 17.0 and the results are as follows; The findings showed differences according to residence type were the intake frequency of processed food, the ways of preservation and the reasons of returning or exchange of the purchased processed food. And there was no difference in the recognition of dietary behavior for processed food and food nutrition labeling system. For overall perception of the dietary behavior and the recognition of the food labeling system, the ratio of college students who considered selection standard related to health when purchasing processed food was low. And the ratio of those who checked the label for milk and dairy products or instant food which is concerned with decomposition was high. On the contrary, the ratio of checking was relatively low for beverages, noodles and cookies. Many answered that the reason for having checked the food label was to find out the safety of the food and that of having read the nutrition label was to control weight and to check the nutrition ingredients. In general, many answered positively for the recognition and the necessity of food labeling system, but the actual practice of selecting and managing processed food was poor.

• Speech Sciences
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• v.10 no.1
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• pp.95-105
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• 2003

This paper describes a new parameter for voice activity detection which serves as a front-end part for automatic speech recognition systems. The new parameter called run-ratio is derived from the runs test statistic which is used in the statistical test for randomness of a given sequence. The run-ratio parameter has the property that the values of the parameter for the random sequence are about 1. To apply the run-ratio parameter into the voice activity detection method, it is assumed that the samples of an inputted audio signal should be converted to binary sequences of positive and negative values. Then, the silence region in the audio signal can be regarded as random sequences so that their values of the run-ratio would be about 1. The run-ratio for the voiced region has far lower values than 1 and for fricative sounds higher values than 1. Therefore, the parameter can discriminate speech signals from the background sounds by using the newly derived run-ratio parameter. The proposed voice activity detector outperformed the conventional energy-based detector in the sense of error mean and variance, small deviation from true speech boundaries, and low chance of missing real utterances

• 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.

• MALSORI
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• no.44
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• pp.83-92
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• 2002

Speech recognition performance is severly degraded under noisy envrionments. One approach to cope with this problem is audio-visual speech recognition. In this paper, we discuss the experiment results of bimodal speech recongition based on enhanced speech feature vectors using lip information. We try various kinds of speech features as like linear predicion coefficient, cepstrum, log area ratio and etc for transforming lip information into speech parameters. The experimental results show that the cepstrum parameter is the best feature in the point of reconition rate. Also, we present the desirable weighting values of audio and visual informations depending on signal-to-noiso ratio.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.117-123
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• 1996

In this paper, we present a new learning method for the fuzzy ARTMAP which is effective for the noisy input patterns. Conventional fuzzy ARTMAP employs only fuzzy AND operation between input vector and weight vector in learning both top-down and bottom-up weight vectors. This fuzzy AND operation causes excessive update of the weight vector in the noisy input environment. As a result, the number of spurious categories are increased and the recognition ratio is reduced. To solve these problems, we propose a new method in updating the weight vectors: the top-down weight vectors of the fuzzy ART system are updated using weighted average of the input vector and the weight vector itself, and the bottom-up weight vectors are updated using fuzzy AND operation between the updated top-down weitht vector and bottom-up weight vector itself. The weighted average prevents the excessive update of the weight vectors and the fuzzy AND operation renders the learning fast and stble. Simulation results show that the proposed method reduces the generation of spurious categories and increases the recognition ratio in the noisy input environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.121-127
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• 2013

A vision based hand-gesture interface method for substituting a pointing device is proposed in this paper, which is used the ratio and size variation of Gesture Region. Proposed method uses the skin hue&saturation of the hand region from the HSI color model to extract the hand region effectively. This method can remove the non-hand region, and reduces the noise effect by the light source. Also, as the computation quantity is reduced by detecting not the static hand-shape recognition, but the ratio and size variation of hand-moving from the clipped hand region in real time, more response speed is guaranteed. In order to evaluate the performance of the our proposed method, after applying to the computerized self visual acuity testing system as a pointing device. As a result, the proposed method showed the average 86% gesture recognition ratio and 87% coordinate moving recognition ratio.