• 한국산업정보학회논문지
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• 제21권4호
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• pp.13-20
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• 2016

현대 사회에서 스마트폰은 장착된 고화질의 카메라를 이용하여 광학문자인식시스템을 구현할 수 있다. 광학문자시스템으로부터 인식된 문자들은 또한 TTS를 이용하여 시각장애인들에게 음성 서비스를 제공할 수 있다. 문자 정보가 들어있는 객체에 대하여 스마트 폰 카메라를 사용하여 촬영하는 것도 시각장애인들에게는 다소 어려운 일이다. 왜냐하면 피사체의 촬영 이미지를 볼 수가 없기 때문이다. 이러한 문제점을 해결하기 위하여 본 논문에서는 스마트폰의 자이로 센서를 사용하여 시각장애인들의 올바른 촬영을 유도하는 방법을 제안한다. 구현된 프로그램을 사용하여 모의 실험한 결과, 제안된 방법은 같은 객체로부터 보다 많은 문자를 인식하는 것을 확인할 수 있었다.

• 전자공학회논문지CI
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• 제37권5호
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• pp.54-63
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• 2000

본 논문에서는 음성인식, 음성합성, Motion Tracking, 3D Animation이 가능한 가상캐릭터를 구현하였다. 음성인식으로는 K-means 128 Level VQ와 MFCC의 특징패턴을 바탕으로 Discrete-HMM 알고리즘을 이용하였다. 음성합성에는 반음절 단위의 TD-PSOLA를 이용하였으며, Motion Tracking에서는 계산량을 줄이기 위해 Fast Optical Flow Like Method를 제안하고, 3D Animation 시스템은 Vertex Interpolation방법으로 Animation을 하고 Direct3D를 이용하여 Rendering을 하였다. 최종적으로 위에 나열된 시스템들을 통합하여 사용자를 계속적으로 주시하면서 사용자와 함께 구구단 게임을 할 수 있는 가상캐릭터를 구현하였다.

• 전자공학회논문지A
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• 제31A권6호
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• pp.61-69
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• 1994

For distortion-invariant recognition of Korean characters, a holographic implementation of an optical associative memory system is proposed. The structure of the proposed system is a single-layer neural network employing interconneclion matrix, thresholding and feedback. To provide the interconnection matrix, we use two CGII's which are placed on intermcdiate plane of cascaded Vander Lugt corrclators to form an optical memory loop. The holographic correlator stores reference images in a hologram and retrives them in a coherently illuminated feedback loop. An input image which maybe noisy or incomplete, is applicd to the system and simultaneously correlated optically with all of the stord images. These correlations are throsholed and fed back to the input, where the strongest correlation reinforces the input image. The enhanced image passes arround the loop repeatedly, approaching the stored image more closely on each pass until the system stabilizes on the desired image. The computer simulation results show that the proposed Korean Character recognition algorithm has high discrimination capability and noise immunity.

• 지능정보연구
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• 제10권3호
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• pp.1-10
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• 2004

본 논문에서는 영문 인쇄체의 접합 문자를 분할하는 새로운 알고리듬을 제안한다. 본 논문에서 제안하는 문자 분할의 접근 방식은 특징을 기반으로 한 접근 방식(feature-based approaches)과 인식을 기반으로 한 접근 방식(recognition-based approaches)의 단점을 보안한 새로운 문자 분할 방법이다. 접합 문자의 측면 윤곽 특징을 정의하고, 그 측면 윤곽 특징을 이용하여 문자 인식의 도움 없이도 접합 문자 내의 문자를 일차 내정하여 분할 한 후 다시 측면 윤곽 특징을 이용하여 문자 분할을 최종 확정한다. 또한 본 논문에서는 분할 비용을 정의하는데, 분할 비용은 최적의 경로로 문자 분할을 수행하도록 한다. 제안된 문자 분할의 성능은 U.S. 메일에서 주소를 자동으로 인식하여 메일을 자동으로 도착지별로 분류하는 시스템(Envelope Reader System)을 이용해 구해졌다. 3359개의 메일이 실험되어졌는데, 제안된 문자 분할 알고리즘에 의해 분류율이 $68.92\%$에서 $80.08\%$로 성능이 향상되었다.

• International Journal of Contents
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• 제12권4호
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• pp.53-61
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• 2016

Although extensively developed, optical music recognition systems have mostly focused on musical symbols (notes, rests, etc.), while disregarding the chord symbols. The process becomes difficult when the images are distorted or slurred, although this can be resolved using optical character recognition systems. Moreover, the appearance of outliers (lyrics, dynamics, etc.) increases the complexity of the chord recognition. Therefore, we propose a new approach addressing these issues. After binarization, un-distortion, and stave and lyric removal of a musical image, a rule-based method is applied to detect the potential regions of chord symbols. Next, a lexicon-driven approach is used to optimally and simultaneously separate and recognize characters. The score that is returned from the recognition process is used to detect the outliers. The effectiveness of our system is demonstrated through impressive accuracy of experimental results on two datasets having a variety of resolutions.

• ETRI Journal
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• 제32권5호
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• pp.729-739
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• 2010

Due to the rapid development of mobile devices equipped with cameras, instant translation of any text seen in any context is possible. Mobile devices can serve as a translation tool by recognizing the texts presented in the captured scenes. Images captured by cameras will embed more external or unwanted effects which need not to be considered in traditional optical character recognition (OCR). In this paper, we segment a text image captured by mobile devices into individual single characters to facilitate OCR kernel processing. Before proceeding with character segmentation, text detection and text line construction need to be performed in advance. A novel character segmentation method which integrates touched character filters is employed on text images captured by cameras. In addition, periphery features are extracted from the segmented images of touched characters and fed as inputs to support vector machines to calculate the confident values. In our experiment, the accuracy rate of the proposed character segmentation system is 94.90%, which demonstrates the effectiveness of the proposed method.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.224-237
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• 2020

Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.6-10
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• 2021

Recognition of characters printed or marked on the PCB components from images captured using cameras is an important task in PCB components inspection systems. Previous optical character recognition (OCR) of PCB components typically consists of two stages: character segmentation and classification of each segmented character. However, character segmentation often fails due to corrupted characters, low image contrast, etc. Thus, OCR without character segmentation is desirable and increasingly used via deep neural networks. Typical implementation based on deep neural nets without character segmentation includes convolutional neural network followed by recurrent neural network (RNN). However, one disadvantage of this approach is slow execution due to RNN layers. LPRNet is a segmentation-free character recognition network with excellent accuracy proved in license plate recognition. LPRNet uses a wide convolution instead of RNN, thus enabling fast inference. In this paper, LPRNet was adapted for recognizing characters printed on PCB components with fast execution and high accuracy. Initial training with synthetic images followed by fine-tuning on real text images yielded accurate recognition. This net can be further optimized on Intel CPU using OpenVINO tool kit. The optimized version of the network can be run in real-time faster than even GPU.

• Journal of Information Processing Systems
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• 제9권1호
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• 전자공학회논문지B
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• 제31B권6호
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• pp.10-16
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• 1994

In case of off-line character recognition with statistical method, the character recognition speed for Korean or Chinese characters is slow since the amount of calculation is huge. To improve this problem, we seperate the recognition steps into several functional stages and implement them with hardwares for each stage so that all the stages can be processed with pipline structure. In accordance with temporal parallel processing, a high speed character recognition system can be implemented. In this paper, we implement a classification hardware, which is one of the several functional stages, to improve the speed by parallel structure with multiple DSPs(Digital Signal Processors). Also, it is designed to be able to expand DSP boards in parallel to make processing faster as much as we wish. We implement the hardware as an add-on board in IBM-PC, and the result of experiment is that it can process about 47-times and 71-times faster with 2 DSPs and 3 DSPs respectively than the IBM-PC(486D$\times$2-66MHz). The effectiveness is proved by developing a high speed OCR(Optical Character Recognizer).