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구근휘(KeunHwi Koo),윤종필(Jong Pil Yun),최성후(SungHoo Choi),최종현(JongHyun Choi),김상우(Sang Woo Kim) 대한전기학회 2008 정보 및 제어 심포지엄 논문집 Vol.2008 No.1
제철소에서 생산된 Slab에는 서로를 구분하기 위해 관리번호가 기재되어 있다. 호스트 컴퓨터에서 보내 온 Slab 관리번호와 제품에 마킹되어 있는 Slab 관리번호의 일치 여부를 확인하기 위하여 자동 인식 시스템이 설치되어 있다. 자동 인식 시스템은 실시간으로 Slab가 없을 때의 영상과 Slab가 있을 때의 영상을 촬영하고 이를 이용하여 Slab 관리 번호를 인식하는 방법으로 구성되어 있다. 제철소 배경 영상이 복잡하고 조명이 계속 바뀌기 때문에 Text Region을 찾는 방법은 Slab 관리 번호를 인식하는데 가장 큰 문제점이다. 본 논문에서는 복잡한 배경을 실시간으로 Training하여 Text Region을 찾기 위한 전처리 과정을 나타내었다. 복잡한 배경 영상을 이용하여 Slab가 위치한 Region을 찾을 수 있고 실시간으로 Training하기 때문에 조명의 영향을 줄일 수 있다.
구근휘(Keunhwi Koo),최성후(SungHoo Choi),윤종필(Jong Pil Yun),최종현(JongHyun Choi),김상우(Sang Woo Kim) 대한전기학회 2009 전기학회논문지 Vol.58 No.4
Character recognition system consists of four step; text localization, text segmentation, character segmentation, and recognition. The character segmentation is very important and difficult because of noise, illumination, and so on. For high recognition rates of the system, it is necessary to take good performance of character segmentation algorithm. Many algorithms for character segmentation have been developed up to now, and many people have been recently making researches in segmentation of touching or overlapping character. Most of algorithms cannot apply to the text regions of management number marked on the slab in steel image, because the text regions are irregular such as touching character by strong illumination and by trouble of nozzle in marking machine, and loss of character. It is difficult to gain high success rate in various cases. This paper describes a new algorithm of character segmentation to recognize slab management number marked on the slab in the steel image. It is very important that pre-processing step is to convert gray image to binary image without loss of character and touching character. In this binary image, non-touching characters are simply separated by using vertical projection profile. For separating touching characters, after we use combined profile to find candidate points of boundary, decide real character boundary by using method based on recognition. In recognition step, we remove noise of character images, then recognize respective character images. In this paper, the proposed algorithm is effective for character segmentation and recognition of various text regions on the slab in steel image.
최종현(Jong Hyun Choi),최성후(Sung Hoo Choi),윤종필(Jong Pil Yun),구근휘(Keunhwi Koo),김상우(Sang Woo Kim) 대한전기학회 2009 전기학회논문지 Vol.58 No.5
In steel making production line, steel slabs are given a unique identification number. This identification number, Slab management number (SMN), gives information about the use of the slab. Identification of SMN has been done by humans for several years, but this is expensive and not accurate and it has been a heavy burden on the workers. Consequently, to improve efficiency, automatic recognition system is desirable. Generally, a recognition system consists of text localization, text extraction, character segmentation, and character recognition. For exact SMN identification, all the stage of the recognition system must be successful. In particular, the text localization is great important stage and difficult to process. However, because of many text-like patterns in a complex background and high fuzziness between the slab and background, directly extracting text region is difficult to process. If the slab region including SMN can be detected precisely, text localization algorithm will be able to be developed on the more simple method and the processing time of the overall recognition system will be reduced. This paper describes about the slab region localization using SIFT (Scale Invariant Feature Transform) features in the image. First, SIFT algorithm is applied the captured background and slab image, then features of two images are matched by Nearest Neighbor (NN) algorithm. However, correct matching rate can be low when two images are matched. Thus, to remove incorrect match between the features of two images, geometric locations of the matched two feature points are used. Finally, search rectangle method is performed in correct matching features, and then the top boundary and side boundaries of the slab region are determined. For this processes, we can reduce search region for extraction of SMN from the slab image. Most cases, to extract text region, search region is heuristically fixed [1][2]. However, the proposed algorithm is more analytic than other algorithms, because the search region is not fixed and the slab region is searched in the whole image. Experimental results show that the proposed algorithm has a good performance.
슬라브 제품 정보 인식을 위한 문자 분리 및 문자 인식 알고리즘 개발
최성후(SungHoo Choi),윤종필(Jong Pil Yun),박영수(YoungSu Park),박지훈(JeeHoon Park),구근휘(KeunHwi Koo),김상우(Sang Woo Kim) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.4
This paper describes about the printed character segmentation and recognition system for slabs in steel manufacturing process. To increase the recognition rate, it is important to improve success rate of character segmentation. Since Slabs' front area surface are not uniform and surface temperature is very high, marked characters not only undergo damages but also have much noise. On the other hand, since almost marked characters are very thick and the space between characters is only about 10 ~ 15 ㎜, there are many touching characters. Therefore appropriate character image preprocessing and segmentation algorithm is needed. In this paper we propose a multi-local thresholding method for damaged character restoration, a modified touching character segmentation algorithm for marked characters. Finally a effective Multi-Class SVM is used to recognize segmented characters.