• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.1-8
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• 2017

Recently, with the development of ICT technology such as cloud and mobile, image utilization through social networks is increasing rapidly. These images contain personal information, and personal information leakage accidents may occur. As a result, studies are underway to recognize and mask personal information in images. However, optical character recognition, which recognizes personal information in images, varies greatly depending on brightness, contrast, and distortion, and Korean recognition is insufficient. Therefore, in this paper, we design and implement a personal information identification and masking system based on image recognition through deep learning application using CNN algorithm based on optical character recognition method. Also, the proposed system and optical character recognition compares and evaluates the recognition rate of personal information on the same image and measures the face recognition rate of the proposed system. Test results show that the recognition rate of personal information in the proposed system is 32.7% higher than that of optical character recognition and the face recognition rate is 86.6%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.33-43
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• 2010

In this paper, we propose a floral scent recognition using odor sensors and a odor display using odor distribution system. Proposed odor recognition has method of correlation coefficient between sensors that select optimal sensors in floral scent recognition system of selective multi-sensors. Proposed floral scent recognition system consists of four module such as floral scent acquisition module, optimal sensor decision module, entropy-based floral scent detection module, and floral scent recognition module. Odor distribution system consists of generation module of distribution information, control module of distribution, output module of distribution. We applied to floral scent recognition for performance evaluation of proposed sensors decision method. As a result, application of proposed method with floral scent recognition obtained recognition rate of 95.67% case of using 16 sensors while applied floral scent recognition system of proposed sensor decision method confirmed recognition rate of 96% using only 8 sensors. Also, we applied to odor display of proposed method and obtained 3.18 thorough MOS experimentation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.149-154
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• 2004

In this research, we selected the speech recognition to implement the electric wheelchair system as a method to control it by only using the speech and used DTW (Dynamic Time Warping), which is speaker-dependent and has a relatively high recognition rate among the speech recognitions. However, it has to have small memory and fast process speed performance under consideration of real-time. Thus, we introduced VQ (Vector Quantization) which is widely used as a compression algorithm of speaker-independent recognition, to secure fast recognition and small memory. However, we found that the recognition rate decreased after using VQ. To improve the recognition rate, we applied ART2 (Adaptive Reason Theory 2) algorithm as a post-process algorithm to obtain about 5% recognition rate improvement. To utilize ART2, we have to apply an error range. In case that the subtraction of the first distance from the second distance for each distance obtained to apply DTW is 20 or more, the error range is applied. Likewise, ART2 was applied and we could obtain fast process and high recognition rate. Moreover, since this system is a moving object, the system should be implemented as an embedded one. Thus, we selected TMS320C32 chip, which can process significantly many calculations relatively fast, to implement the embedded system. Considering that the memory is speech, we used 128kbyte-RAM and 64kbyte ROM to save large amount of data. In case of speech input, we used 16-bit stereo audio codec, securing relatively accurate data through high resolution capacity.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.915-925
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• 1996

In this paper, in order to find effective features which can handle variations in off-line handwritten numerals, we performed a comparative study on various sets of features. Results of experimental performance comparison shows that 4- directional features using contours and features which combined cross distance, cross, mesh and projection features are very effective for off-line handwritten numerals recognition in terms of recognition rates and recognition time. And in order to surmount limitation of recognition rate by a single neural network. we proposed a modularized neural network using majority voting and reliability factor with complex feature that mix effective features together. In order to verify the performance of the proposed method, the handwritten numeral databases of Concordia University of Canada and Dong-A University of Korea are used in the experiments. With the database of Concordia University, the recognition rate of 97.1%, the rejection rate of 1.5%, the error rate of 1.4% and the reliability of 98.5% are obtained ; and with the database of Dong-A University, there cognition rate of 98%, the rejection rate of 1.2%, the error rate of 0.8%, the reliability o99.1% are obtained.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.522-532
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• 1995

Multilayer neural networks with backpropagation learning algorithm are widely used for pattern classification problems. For many real applications, it is more important to reduce the misclassification rate than to increase the rate of successful classification. But multilayer perceptrons(MLP's) have drawbacks of slow learning speed and false convergence to local minima. In this paper, we propose a new method for character recognition problems with a single-layer network and double rejection mechanisms, which guarantees a very low misclassification rate. Comparing to the MLP's, it yields fast learning and requires a simple hardware architecture. We also introduce a new coding scheme to reduce the misclassification rate. We have prepared two databases: one with 135,000 digit patterns and the other with 117,000 letter patterns, and have applied the proposed method for printed character recognition, which shows that the method reduces the misclassification rate significantly without sacrificing the correct recognition rate.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.195-198
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• 2003

The accuracy of automatic speech recognition system depends on the presence of background noise and speaker variability such as sex, intonation of speech, and speaking rate. Specially, the speaking rate of both inter-speaker and intra-speaker is a serious cause of mis-recognition. In this paper, we propose the compensation method of the speaking rate by the ratio of each vowel's length in a phrase. First the number of feature vectors in a phrase is estimated by the information of speaking rate. Second, the estimated number of feature vectors is assigned to each syllable of the phrase according to the ratio of its vowel length. Finally, the process of feature vector extraction is operated by the number that assigned to each syllable in the phrase. As a result the accuracy of automatic speech recognition was improved using the proposed compensation method of the speaking rate.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.496-503
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• 2010

Speech recognition system is developed of standalone, In case of a mobile terminal using that low recognition rate represent because of limitation of memory size and audio compression. This study suggest vocabulary recognition highest performance improvement system for separate acoustic search and lexical search. Acoustic search is carry out in mobile terminal, lexical search is carry out in server processing system. feature vector of speech signal extract using GMM a phoneme execution, recognition a phoneme list transmission server using Lexical Tree Search algorithm lexical search recognition execution. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.71%, represent recognition speed of 1.58 second.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.273-278
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• 2014

In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. Improve them with a HMM model is proposed for the optimization of the Bayesian methods. In this paper is posterior distribution and prior distribution in recognition Gaussian mixtures model provides a model to optimize of the Bayesian methods vocabulary recognition. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.1-6
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• 2022

As augmented reality technology becomes more common and prevelant, marker-based AR contents are applied in various ways. However AR contents are still hardly utilized due to the low recognition rate of marker images. In order to increase the recognition rate of AR marker images, this paper experiment and analyzed how much the recognition rate of markers could be improved when image correction and design changes was applied. The experimental result shows that the image correction task could significantly improve the number of image characteristics and the recognition grade if the image was modified in a way its saturation value is increased. Moreover, the recognition rate was improved even more when regular pattern design was added to the original marker image. In conclusion, it was possible to make the marker well recognized through proper correction of the image and additional process of pattern design in the process of producing the marker image.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.199-202
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• 2000

Speaking Rate has variety depends on the situation and habit of speakers. It has been many studied about speaking rate In speaker recognition. The study of speaking rate in speech recognition is one of considerable matter when It is recognized the speakers and it is measured by many speech data base and complicate estimation for accuracy. In this paper, conventional vocoder process the speech signal when encoding and transmitting without regard to speaking rate so in order to apply the speaking rate for vocoder It should be considered the simpler algorithm and less computation amount than the conventional method of speaking rate used In speech recognition. We proposed the speaking rate algorithm which is used the simple parameter with Line Spectrum Pair (LSP). The proposed peaking rate method is measured by the information of LSP in speech. We measured the variety rate of phenomenon about utterances which have different velocity, respectively. As a result, It has distinct variation rate of phenomenon between utterances uttered fast and slow and the rate is 42.8% higher in case of uttered fast than in case of uttered slow.