• Title, Summary, Keyword: recognition

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Research on Korea Text Recognition in Images Using Deep Learning (딥 러닝 기법을 활용한 이미지 내 한글 텍스트 인식에 관한 연구)

  • Sung, Sang-Ha;Lee, Kang-Bae;Park, Sung-Ho
    • Journal of the Korea Convergence Society
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    • v.11 no.6
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    • pp.1-6
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    • 2020
  • In this study, research on character recognition, which is one of the fields of computer vision, was conducted. Optical character recognition, which is one of the most widely used character recognition techniques, suffers from decreasing recognition rate if the recognition target deviates from a certain standard and format. Hence, this study aimed to address this limitation by applying deep learning techniques to character recognition. In addition, as most character recognition studies have been limited to English or number recognition, the recognition range has been expanded through additional data training on Korean text. As a result, this study derived a deep learning-based character recognition algorithm for Korean text recognition. The algorithm obtained a score of 0.841 on the 1-NED evaluation method, which is a similar result to that of English recognition. Further, based on the analysis of the results, major issues with Korean text recognition and possible future study tasks are introduced.

Performance Analysis of Face Image Recognition System Using A R T Model and Multi-layer perceptron (ART와 다층 퍼셉트론을 이용한 얼굴인식 시스템의 성능분석)

  • 김영일;안민옥
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.30B no.2
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    • pp.69-77
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    • 1993
  • Automatic image recognition system is essential for a better man-to machine interaction. Because of the noise and deformation due to the sensor operation, it is not simple to build an image recognition system even for the fixed images. In this paper neural network which has been reported to be adequate for pattern recognition task is applied to the fixed and variational(rotation, size, position variation for the fixed image)recognition with a hope that the problems of conventional pattern recognition techniques are overcome. At fixed image recognition system. ART model is trained with face images obtained by camera. When recognizing an matching score. In the test when wigilance level 0.6 - 0.8 the system has achievel 100% correct face recognition rate. In the variational image recognition system, 65 invariant moment features sets are taken from thirteen persons. 39 data are taken to train multi-layer perceptron and other 26 data used for testing. The result shows 92.5% recognition rate.

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A Factor Analysis for the Success of Commercialization of the Facial Extraction and Recognition Image Information System (얼굴추출 및 인식 영상정보 시스템 상용화 성공요인 분석)

  • Kim, Shin-Pyo;Oh, Se-Dong
    • Journal of Industrial Convergence
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    • v.13 no.2
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    • pp.45-54
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    • 2015
  • This Study aims to analyze the factors for the success of commercialization of the facial extraction and recognition image security information system of the domestic companies in Korea. As the results of the analysis, the internal factors for the success of commercialization of the facial extraction and recognition image security information system of the company were found to include (1) Holding of technology for close range facial recognition, (2) Holding of several facial recognition related patents, (3) Preference for the facial recognition security system over the fingerprint recognition and (4) strong volition of the CEO of the corresponding company. On the other hand, the external environmental factors for the success were found to include (1) Extensiveness of the market, (2) Rapid growth of the global facial recognition market, (3) Increased demand for the image security system, (4) Competition in securing of the engine for facial extraction and recognition and (5) Selection by the government as one of the 100 major strategic products.

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Implementation of Real-time Wheel Order Recognition System Based on the Predictive Parameters for Speaker's Intention

  • Moon, Serng-Bae;Jun, Seung-Hwan
    • Journal of Navigation and Port Research
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    • v.35 no.7
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    • pp.551-556
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    • 2011
  • In this paper new enhanced post-process predicting the speaker's intention was suggested to implement the real-time control module for ship's autopilot using speech recognition algorithm. The parameter was developed to predict the likeliest wheel order based on the previous order and expected to increase the recognition rate more than pre-recognition process depending on the universal speech recognition algorithms. The values of parameter were assessed by five certified deck officers being good at conning vessel. And the entire wheel order recognition process were programmed to TMS320C5416 DSP so that the system could recognize the speaker's orders and control the autopilot in real-time. We conducted some experiments to verify the usefulness of suggested module. As a result, we have confirmed that the post-recognition process module could make good enough accuracy in recognition capabilities to realize the autopilot being operated by the speech recognition system.

A Multimodal Emotion Recognition Using the Facial Image and Speech Signal

  • Go, Hyoun-Joo;Kim, Yong-Tae;Chun, Myung-Geun
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.1
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    • pp.1-6
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    • 2005
  • In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facia] expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.

Recognition of Emotion and Emotional Speech Based on Prosodic Processing

  • Kim, Sung-Ill
    • The Journal of the Acoustical Society of Korea
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    • v.23 no.3E
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    • pp.85-90
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    • 2004
  • This paper presents two kinds of new approaches, one of which is concerned with recognition of emotional speech such as anger, happiness, normal, sadness, or surprise. The other is concerned with emotion recognition in speech. For the proposed speech recognition system handling human speech with emotional states, total nine kinds of prosodic features were first extracted and then given to prosodic identifier. In evaluation, the recognition results on emotional speech showed that the rates using proposed method increased more greatly than the existing speech recognizer. For recognition of emotion, on the other hands, four kinds of prosodic parameters such as pitch, energy, and their derivatives were proposed, that were then trained by discrete duration continuous hidden Markov models(DDCHMM) for recognition. In this approach, the emotional models were adapted by specific speaker's speech, using maximum a posteriori(MAP) estimation. In evaluation, the recognition results on emotional states showed that the rates on the vocal emotions gradually increased with an increase of adaptation sample number.

Digit Recognition using Speech and Image Information (음성과 영상 정보를 이용한 우리말 숫자음 인식)

  • 이종혁;최재원
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.1
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    • pp.83-88
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    • 2002
  • In the majority of case, speech recognition method tried recognition using only speech information In order to highten the recognition rate, we proposed recognition system that recognige digit using speech and image information. Through an experiment, this paper compared the recognition rate performed by existent speech recognition method and speech recognition method that includes image information. When we added the image information to the speech information, the speech recognition rate was increased about 6%. This paper shows that adding image information to speech information is more effective than using only speech information In digit recognition.

A Study On Parameter Measurement for Artificial Intelligence Object Recognition (인공지능 객체인식에 관한 파라미터 측정 연구)

  • Choi, Byung Kwan
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.15 no.3
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    • pp.15-28
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    • 2019
  • Artificial intelligence is evolving rapidly in the ICT field, smart convergence media system and content industry through the fourth industrial revolution, and it is evolving very rapidly through Big Data. In this paper, we propose a face recognition method based on object recognition based on object recognition through artificial intelligence. In this method, Were experimented and studied through the object recognition technique of artificial intelligence. In the conventional 3D image field, general research on object recognition has been carried out variously, and researches have been conducted on the side effects of visual fatigue and dizziness through 3D image. However, in this study, we tried to solve the problem caused by the quantitative difference between object recognition and object recognition for human factor algorithm that measure visual fatigue through cognitive function, morphological analysis and object recognition. Especially, The new method of computer interaction is presented and the results are shown through experiments.

Context Awareness Model using the Improved Google Activity Recognition (개선된 Google Activity Recognition을 이용한 상황인지 모델)

  • Baek, Seungeun;Park, Sangwon
    • KIPS Transactions on Software and Data Engineering
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    • v.4 no.1
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    • pp.57-64
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    • 2015
  • Activity recognition technology is gaining attention because it can provide useful information follow user's situation. In research of activity recognition before smartphone's dissemination, we had to infer user's activity by using independent sensor. But now, with development of IT industry, we can infer user's activity by using inner sensor of smartphone. So, more animated research of activity recognition is being implemented now. By applying activity recognition system, we can develop service like recommending application according to user's preference or providing information of route. Some previous activity recognition systems have a defect using up too much energy, because they use GPS sensor. On the other hand, activity recognition system which Google released recently (Google Activity Recognition) needs only a few power because it use 'Network Provider' instead of GPS. Thus it is suitable to smartphone application system. But through a result from testing performance of Google Activity Recognition, we found that is difficult to getting user's exact activity because of unnecessary activity element and some wrong recognition. So, in this paper, we describe problems of Google Activity Recognition and propose AGAR(Advanced Google Activity Recognition) applied method to improve accuracy level because we need more exact activity recognition for new service based on activity recognition. Also to appraise value of AGAR, we compare performance of other activity recognition systems and ours and explain an applied possibility of AGAR by developing exemplary program.

Design and Implementation of a Bimodal User Recognition System using Face and Audio (얼굴과 음성 정보를 이용한 바이모달 사용자 인식 시스템 설계 및 구현)

  • Kim Myung-Hun;Lee Chi-Geun;So In-Mi;Jung Sung-Tae
    • Journal of the Korea Society of Computer and Information
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    • v.10 no.5
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    • pp.353-362
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    • 2005
  • Recently, study of Bimodal recognition has become very active. In this paper we propose a Bimodal user recognition system that uses face information and audio information. Face recognition consists of face detection step and face recognition step. Face detection uses AdaBoost to find face candidate area. After finding face candidates, PCA feature extraction is applied to decrease the dimension of feature vector. And then, SVM classifiers are used to detect and recognize face. Audio recognition uses MFCC for audio feature extraction and HMM is used for audio recognition. Experimental results show that the Bimodal recognition can improve the user recognition rate much more than audio only recognition, especially in the Presence of noise.

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