• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.175-186
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• 2004

We present a hybrid approach of texture-based method and connected component (CC)-based method for text extraction in complex images. Two primary methods, which are mainly utilized in this area, are sequentially merged for compensating for their weak points. An automatically constructed MLP-based texture classifier can increase recall rates for complex images with small amount of user intervention and without explicit feature extraction. CC-based filtering based on the shape information using NMF enhances the precision rate without affecting overall performance. As a result, a combination of texture and CC-based methods leads to not only robust but also efficient text extraction. We also enhance the processing speed by adopting appropriate region marking methods for each input image category.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.765-768
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• 2003

금속이나 암석에 음각(陰角)으로 각인된 문자나 그림들은 날씨나 주변 빛 환경에 따라 시각으로 입력되는 정보에 큰 차이를 보인다. 이를 이미지검출장치를 통해 읽어드려 디지털 이미지 신호로 만들고자 할 때는 더욱 그 정도가 심하여 대상체가 위치하는 빛 환경이나 검출기 특성에 각별한 신경을 써야한다. 자연광이나 전구 그리고 기후나 날씨에 의해 조성되는 빛 환경은 조도(照度), 조사각도(照射角度), 그림자 및 대상체 표면 상태 등이 중요한 결정 인자들이다. 빛 환경이 디지털 이미지 질(質)에 끼치는 영향을 최소화하기 위한 실험실 차원의 빛환경조정실을 구축하였다. 외부 유입 광선을 모두 차단하고 지향성이 있는 조명에 의해서만 대상체에 빛이 조사되도록 하고 디지털 카메라로 대상체의 이미지를 담았다. 음각 문자를 새긴 샘플석문(石文)을 제작하고 실험실 안의 정량화된 빛환경 하에서 석문의 이미지를 취득하였다. 전처리 과정을 통해 노이즈를 제거하고 이미지의 질을 향상시켰다. 처리된 이미지를 분석하여 문자영역과 바탕영역의 신호패턴을 추출한 다음 룩업 테이블, 조도 레벨 슬라이징, 중첩의 원리 및 Morphology 등의 기법을 알고리즘화하여 2진 형태의 음각문자를 판독 및 복원하는데 성공하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.383-385
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• 2000

차량 번호판 문자영상으로부터 여러 가지 특징 추출 방법을 조합하여 입력특징소를 재구성하고, 신경망을 이용하여 문자를 인식한다. 속도 개선을 위해 특별한 전처리 과정없이 이치화와 크기 정규화만을 수행한 후 그물망 방법과 BLT 방법, 정규화된 투영값 특정 방법을 조합하여 입력특징소를 구성한다. 본 연구에서는 숫자 인식에서 그물망 방법과 BLT 방법을 이용하여 잡음으로 인한 유사 문자의 오인식을 해결하였고, 문자 인식에서는 정규화된 투영값 특징을 이용하여 문자의 유형을 분류한 후 자소를 개별적으로 인식하였다. 이로써 모음 인식 경우에 중요한 역할을 하는 작은 획의 영역에 BLT 방법을 사용함으로 기존 연구에서의 모음 오인식 문제를 해결하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.328-330
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• 2001

컴퓨터 비전을 이용한 자동차 번호판 인식은 자동차에 특별한 장치가 필요없어 비용면에서 유리하다. 자동차 번호판을 인식하기 위해서는 번호판의 영역을 추출한 후 번호판에서 문자와 숫자 영역을 분리하고 그 세그먼트를 신경 회로망 혹은 다른 방법을 통하여 인식한다. 본 논문은 자동차 번호판 위치 검출 방법과 세그멘테이션 방법에 대하여 제안한다. 자동차 번호판 위치 검출을 위하여 모폴로지 기법과 ART2 클러스터링 방법을 이용하였고 검출한 번호판 영역에서 세그멘테이션은 모폴로지 연산을 이용한 이진화와 레이블링을 이용한다.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.767-774
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• 2002

Conventional research on image restoration has focused on restoring degraded images resulting from image formation, storage and communication, mainly in the signal processing field. Related research on recovering original image information of caption regions includes a method using BMA(block matching algorithm). The method has problem with frequent incorrect matching and propagating the errors by incorrect matching. Moreover, it is impossible to recover the frames between two scene changes when scene changes occur more than twice. In this paper, we propose a method for recovering original images using EBMA(Extended Block Matching Algorithm) and a region compensation method. To use it in original image recovery, the method extracts a priori knowledge such as information about scene changes, camera motion and caption regions. The method decides the direction of recovery using the extracted caption information(the start and end frames of a caption) and scene change information. According to the direction of recovery, the recovery is performed in units of character components using EBMA and the region compensation method. Experimental results show that EBMA results in good recovery regardless of the speed of moving object and complexity of background in video. The region compensation method recovered original images successfully, when there is no information about the original image to refer to.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1113-1118
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• 2002

In recognition system of the car license plate, the most important is to extract the image of the license plate from a car image. In this paper, we use ACL (Adaptive Color Luminance) algorithm to extract the license plate image from a car image. The ACL algorithm that uses color and luminance information of a car image is used to extract the image of the license plate. In this paper, color, luminance and other related information of a car image are used to extract the image of the license plate from that of a car. In this reason, we call it the ACL algorithm. The ACL algorithm uses color, luminance information and other related information of a license plate. These informations are avaliable to exact the image of the license plate. The rate of extracting the image of the license plate from a car is 97%. The experimental result of the ACL algorithm for the character region is 92%.

• Journal of KIISE:Software and Applications
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• v.32 no.2
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• pp.85-98
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• 2005

Recently, a lot of researches about mobile vision using Personal Digital Assistant(PDA) has been attempted. Many CPUs for PDA are integer CPUs, which have no floating-computation component. It results in slow computation of the algorithms peformed by vision system or image processing, which have much floating-computation. In this paper, in order to resolve this weakness, we propose the Client(PDA)/server(PC) architecture which is connected to each other with a wireless LAN, and we construct the system with pipelining processing using two CPUs of the Client(PDA) and the Server(PC) in image sequence. The Client(PDA) extracts tentative text regions using Edge Density(ED). The Server(PC) uses both the Multi-1.aver Perceptron(MLP)-based texture classifier and Connected Component(CC)-based filtering for a definite text extraction based on the Client(PDA)'s tentativel99-y extracted results. The proposed method leads to not only efficient text extraction by using both the MLP and the CC, but also fast running time using Client(PDA)/server(PC) architecture with the pipelining processing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2276-2290
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• 1994

In this paper, we propose an algorithm for character recognition using the subdivided type and the stable structure information. The subdivided type of character is acquired from the stable structure information of character which is extracted from an input character. Firstly, the character is obtained from a scanner and classified into on of 6 types by using directional density vector. And then, the stable structure information is extracted from each character and the character is subdivided into on of 26 types. Finally, the classified character is recognized by using neural network which is inputted the directional density vector equivalent to JASO area or recognized direct. Aa a result of experiment with KS C 5601 2350 printed Hangeul characters, we obtain the recognition rate of 94%.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.73-81
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• 2007

This paper presents how to recognize the new-type vehicle license plate using multi-link recognizer after extract the features from characters. In order to assist this task, this paper proposed FE-MCBP to recognize each character that got through image preprocess, extract range of vehicle license plate and extract process of each character. FE-MCBP is the recognizer based on the features of the character, The recognizer is employed to identify the new-type vehicle licence plates which have both the hangul and the arabic numeral characters. And its recognition rate is improved 9.7 percent than the back propagation recognizer before. Also it makes use of extract of linear component and region coordinate generation technology to normalize a image of the tilted vehicle license plate. The recognition system of the new-type vehicle license plate make possible recognize a image of the tilted vehicle license plate when using this system. Also, this system can recognize the tilted or imperfect vehicle licence plates.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.1-9
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• 2014

Recently, a study is conducting to image collection and auto detection of attribute information using mobile mapping system. The road sign attribute information detection is difficult because of various size and placement, interference of other facilities like trees. In this study, a text detection method that does not rely on a Korean character template is required to successfully detect the target text when a variety of differently sized texts are present near the target texts. To overcome this, the method of incremental right-to-left blob projection (IRBP) was suggested as a solution; the potential and improvement of the method was also assessed. To assess the performance improvement of the IRBP that was developed, the IRBP method was compared to the existing method that uses Korean templates through the 60 videos of street signs that were used. It was verified that text detection can be improved with the IRBP method.