• 스마트미디어저널
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• 제1권4호
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• pp.59-71
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• 2012

Since mobile phones are used as common communication devices, their applications are increasingly important to human's life. Using smart-phones camera to collect daily life environment's information is one of targets for many applications such as text recognition, object recognition or context awareness. Studies have been conducted to provide important information through the recognition of texts, which are artificially or naturally included in images and movies acquired from mobile phones. In this study, a character segmentation method that improves character-recognition accuracy in images obtained from mobile phone cameras is proposed. The proposed method first classifies texts in a given image to printed letters and handwritten letters since segmentation approaches for them are different. For printed letters, rough segmentation process is conducted, then the segmented regions are integrated, deleted, and re-segmented. Segmentation for the handwritten letters is performed after skews are corrected and the characters are classified by integrating them. The experimental result shows our method achieves a successful performance for both printed and handwritten letters as 95.9% and 84.7%, respectively.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2004년도 추계학술대회
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• pp.163-165
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• 2004

An identification of a font allows that an OCR system can perform font-specific processes, which consist of various mono-font segmentation tools and recognizers According to the font styles, character segmentation method should be applied differently. Touching characters in slant style cannot be segmented vertically but segmented on a slant. This paper proposes that touching characters in italic style can be segmented vertically after slant normalization.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 추계학술대회
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• pp.887-890
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• 2012

Image recognition is one of the most important research directions of pattern recognition. Image based road automatic identification technology is widely used in current society, the intelligence has become the trend of the times. This paper studied the image segmentation algorithm theory and its application in road signs recognition system. With the help of image processing technique, respectively, on road signs automatic recognition algorithm of three main parts, namely, image segmentation, character segmentation, image and character recognition, made a systematic study and algorithm. The experimental results show that: the image segmentation algorithm to establish road signs recognition model, can make effective use of smart phone system and application.

• ETRI Journal
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• 제40권6호
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• pp.774-787
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• 2018

Arabic handwriting segmentation and recognition is an area of research that has not yet been fully understood. Dealing with Arabic ligature segmentation, where the Arabic characters are connected and unconstrained naturally, is one of the fundamental problems when dealing with the Arabic script. Arabic character-recognition techniques consider ligatures as new classes in addition to the classes of the Arabic characters. This paper introduces an enhanced technique for Arabic handwriting recognition using the deep belief network (DBN) and a new morphological algorithm for ligature segmentation. There are two main stages for the implementation of this technique. The first stage involves an enhanced technique of the Sari segmentation algorithm, where a new ligature segmentation algorithm is developed. The second stage involves the Arabic character recognition using DBNs and support vector machines (SVMs). The two stages are tested on the IFN/ENIT and HACDB databases, and the results obtained proved the effectiveness of the proposed algorithm compared with other existing systems.

• 정보처리학회논문지B
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• 제13B권2호
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• pp.89-96
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• 2006

본 논문에서는 한글 문서 영상에서의 문자 분할을 위한 2가지 알고리즘을 제안한다. 첫째는 투영 프로파일 기반 개선된 문자 분할 알고리즘이다. 이 알고리즘은 크게 문자수 추정, 분할 점 획득 및 문자 경계 탐색, 그리고 최적의 문자 분할 결과 선택으로 구성된다. 두 번째는 근접한 문자들이 서로 연결된 저 품질 문서 영상에 적합한 분할 알고리즘이다. 이 경우 연결요소를 제거하기 위해 투영 프로파일의 일부를 잘랐는데, 이를 ${\alpha}$-cut이라 한다. 그 후 전자의 방법을 변형하여 문자 분할을 수행한다. 다양한 폰트 속성을 갖고 품질이 낮은 43,572개의 한글 단어 영상을 대상으로 실험한 결과, 투영 프로파일 기반 개선된 문자 분할 알고리즘이 91.81%, 투영 프로파일에 ${\alpha}$-cut을 적용한 알고리즘이 99.57% 의 문자 분할 성공률을 나타내어 저 품질 한글 문서 영상에서 ${\alpha}$-cut을 이용한 문자 분할 알고리즘이 효과적임을 입증하였다.

• 한국컴퓨터정보학회논문지
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• 제11권3호
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• pp.73-78
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• 2006

기존의 문자 영역 추출 방법은 전체 영상으로부터 컬러 영역 분할이나 프레임 차 방법을 이용하였다. 이들 방법은 휴리스틱에 많이 의존하므로 추출하려는 문자의 사전 정보를 가지고 있어야한다는 점과 구현에 많은 어려움이 존재한다. 본 논문에서는 휴리스틱한 부분을 줄이고 알고리즘을 단순화한 방법을 제안하고자 한다 문자의 지형학적 특징점을 추출하고 이 점들을 MST(Minimum Spanning Tree)를 형성하여 문자의 후보 영역을 추출한다. 문자 영역을 후보 영역의 검증을 통하여 추출한다. 실험 결과 문자의 후보 영역 추출율은 100%이었으며 최종 문자 영역 추출율은 98.2%이었다. 또한 복잡한 영상에서 존재하는 문자 영역도 잘 추출됨을 볼 수 있다.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.411-427
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• 2022

Digging historical and cultural information from seals in ancient books is of great significance. However, ancient Chinese seal samples are scarce and carving methods are diverse, and traditional digital image processing methods based on greyscale have difficulty achieving superior segmentation and recognition performance. Recently, some deep learning algorithms have been proposed to address this problem; however, current neural networks are difficult to train owing to the lack of datasets. To solve the afore-mentioned problems, we proposed an SEL-RefineMask which combines selector of feature pyramid network (SEL-FPN) with RefineMask to segment and recognize seals. We designed an SEL-FPN to intelligently select a specific layer which represents different scales in the FPN and reduces the number of anchor frames. We performed experiments on some instance segmentation networks as the baseline method, and the top-1 segmentation result of 64.93% is 5.73% higher than that of humans. The top-1 result of the SEL-RefineMask network reached 67.96% which surpassed the baseline results. After segmentation, a vision transformer was used to recognize the segmentation output, and the accuracy reached 91%. Furthermore, a dataset of seals in ancient Chinese books (SACB) for segmentation and small seal font (SSF) for recognition were established which are publicly available on the website.

• 반도체디스플레이기술학회지
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• 제11권2호
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• pp.27-31
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• 2012

This paper proposes a new method of character segmentation in a grayscale image using the standard deviation. Firstly, the proposed method scans vertically the region of interest in an image in order to calculate a standard deviation for each scan line. Characters' standard deviations are much bigger than the background's. Therefore, it is possible to segment characters vertically using the differentiation of those two types of standard deviations. Secondly, the method scans each vertically segmented image horizontally at this time, and then segments each image similarly. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiments were conducted by using credit card images. The results show that the proposed algorithm is quite successful for most credit cards. However, the method fails in some credit cards with strong background patterns.

• 정보처리학회논문지B
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• 제12B권5호
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• pp.521-526
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• 2005

본 논문에서는 저 품질의 한글 문서 영상에서 OCR 기반 검색 시스템의 대안으로 키워드 검출 시스템(Keyword Spotting)을 제안하고 OCR 기반 문서 검색 시스템과 비교한다. 제안 시스템은 문자 분할, 키워드 특징 추출 그리고 단어 매칭으로 구성된다. 문자 분할 단계에서는 인접한 두 문자간의 연결을 효과적으로 분리하면서 문자 넓이 값의 분산이 최소가 되도록 하는 문자 분할 방법을 제안한다. 키워드 특징은 서체별 문자 모델의 결합으로 구성한다. 단어 매칭 단계에서는 문자 매칭에 기반한 단어 대 단어 매칭 방법을 적용한다. 본 논문에서 제안한 키워드 검출 시스템의 성능을 평가하기 위해 한글 문서 영상을 대상으로 OCR 기반 문서 검색 시스템과 비교하였다. 그 결과 한글 글자 크기가 작고 문서의 상태가 좋지 않은 경우 제안한 키워드 검출 시스템에 의한 검색 성능이 OCR 기반 검색 시스템 보다 우수함을 입증하였다.

• Journal of Information Processing Systems
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• 제12권4호
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• pp.661-680
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• 2016

The vehicle license plate recognition (VLPR) system analyzes and monitors the speed of vehicles, theft of vehicles, the violation of traffic rules, illegal parking, etc., on the motorway. The VLPR consists of three major parts: license plate detection (LPD), license plate character segmentation (LPCS), and license plate character recognition (LPCR). This paper presents an efficient method for the LPCS and LPCR of Korean vehicle license plates (LPs). LP tilt adjustment is a very important process in LPCS. Radon transformation is used to correct the tilt adjustment of LP. The global threshold segmentation method is used for segmented LP characters from two different types of Korean LPs, which are a single row LP (SRLP) and double row LP (DRLP). The cross-correlation matching method is used for LPCR. Our experimental results show that the proposed methods for LPCS and LPCR can be easily implemented, and they achieved 99.35% and 99.85% segmentation and recognition accuracy rates, respectively for Korean LPs.