• Title/Summary/Keyword: 문자 군집분석

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A Robust Backpropagation Algorithm and It's Application (문자인식을 위한 로버스트 역전파 알고리즘)

  • Oh, Kwang-Sik;Kim, Sang-Min;Lee, Dong-No
    • Journal of the Korean Data and Information Science Society
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    • v.8 no.2
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    • pp.163-171
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    • 1997
  • Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data we employed. When errorneous traning data are employed, the learned mapping can oscillate badly between data points. In this paper we propose a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process, that is stable under small noise perturbation and robust against gross errors.

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Text extraction in images using simplify color and edges pattern analysis (색상 단순화와 윤곽선 패턴 분석을 통한 이미지에서의 글자추출)

  • Yang, Jae-Ho;Park, Young-Soo;Lee, Sang-Hun
    • Journal of the Korea Convergence Society
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    • v.8 no.8
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    • pp.33-40
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    • 2017
  • In this paper, we propose a text extraction method by pattern analysis on contour for effective text detection in image. Text extraction algorithms using edge based methods show good performance in images with simple backgrounds, The images of complex background has a poor performance shortcomings. The proposed method simplifies the color of the image by using K-means clustering in the preprocessing process to detect the character region in the image. Enhance the boundaries of the object through the High pass filter to improve the inaccuracy of the boundary of the object in the color simplification process. Then, by using the difference between the expansion and erosion of the morphology technique, the edges of the object is detected, and the character candidate region is discriminated by analyzing the pattern of the contour portion of the acquired region to remove the unnecessary region (picture, background). As a final result, we have shown that the characters included in the candidate character region are extracted by removing unnecessary regions.

Word Image Decomposition from Image Regions in Document Images using Statistical Analyses (문서 영상의 그림 영역에서 통계적 분석을 이용한 단어 영상 추출)

  • Jeong, Chang-Bu;Kim, Soo-Hyung
    • The KIPS Transactions:PartB
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    • v.13B no.6 s.109
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    • pp.591-600
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    • 2006
  • This paper describes the development and implementation of a algorithm to decompose word images from image regions mixed text/graphics in document images using statistical analyses. To decompose word images from image regions, the character components need to be separated from graphic components. For this process, we propose a method to separate them with an analysis of box-plot using a statistics of structural components. An accuracy of this method is not sensitive to the changes of images because the criterion of separation is defined by the statistics of components. And then the character regions are determined by analyzing a local crowdedness of the separated character components. finally, we devide the character regions into text lines and word images using projection profile analysis, gap clustering, special symbol detection, etc. The proposed system could reduce the influence resulted from the changes of images because it uses the criterion based on the statistics of image regions. Also, we made an experiment with the proposed method in document image processing system for keyword spotting and showed the necessity of studying for the proposed method.

SOM-based Spatio-Temporal Data Mining System (SOM 기반 시공간 데이터 마이닝 시스템)

  • Kang Juyoung;Lee Bongjae;Song Jaeju;Shin Jinho;Yong Hwanseung
    • Proceedings of the Korea Information Processing Society Conference
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    • 2004.11a
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    • pp.105-108
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    • 2004
  • 데이터 양이 급증함에 따라 축적된 데이터로부터 의미있는 지식을 추출해 내고자 하는 데이터 마이닝에 대한 연구가 활발하게 진행되어 왔다. 특히 최근, 환경이 이동 분산화 되어감에 따라 감시${\cdot}$모니터링 시스템, 기상 관측 시스템, GPS 시스템과 같은 다양한 응용 시스템으로부터 방대한 양의 시공간 데이터가 발생하게 되었고, 이른 효율적으로 분석하고자 하는 시공간 데이터 마이닝 연구에 대한 관심이 더욱 높아지고 있다. 기존의 데이터 마이닝 기법의 경우 문자나 숫자 데이터를 대상으로 최적화 되어있기 때문에 시${\cdot}$공간 속성을 동시에 가지는 데이터를 분석하기에는 한계가 있는 것이 사실이다. 본 논문에서는 SOM(Self-Organizing Map)을 적용하여 시공간 클러스터링 모듈을 개발하고, 개발된 모듈의 성능 및 클러스터링 정확성을 다른 세 가지 군집분석 알고리즘과 비교, 분석하였다. 또한 가시화 모듈을 개발하여 입력 데이터의 특성과 결과를 더욱 정확하게 분석할 수 있도록 하였다.

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Text Region Detection Method in Mobile Phone Video (휴대전화 동영상에서의 문자 영역 검출 방법)

  • Lee, Hoon-Jae;Sull, Sang-Hoon
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.47 no.5
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    • pp.192-198
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    • 2010
  • With the popularization of the mobile phone with a built-in camera, there are a lot of effort to provide useful information to users by detecting and recognizing the text in the video which is captured by the camera in mobile phone, and there is a need to detect the text regions in such mobile phone video. In this paper, we propose a method to detect the text regions in the mobile phone video. We employ morphological operation as a preprocessing and obtain binarized image using modified k-means clustering. After that, candidate text regions are obtained by applying connected component analysis and general text characteristic analysis. In addition, we increase the precision of the text detection by examining the frequency of the candidate regions. Experimental results show that the proposed method detects the text regions in the mobile phone video with high precision and recall.

Classification of Climate Zones in South Korea Considering both Air Temperature and Rainfall (기온과 강수특성을 고려한 남한의 기후지역구분)

  • Park, Chang-Yong;Choi, Young-Eun;Moon, Ja-Yeon;Yun, Won-Tae
    • Journal of the Korean Geographical Society
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    • v.44 no.1
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    • pp.1-16
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    • 2009
  • This study aims to classify climate zones using Empirical Orthogonal Function and clustering analyses considering both air temperature and rainfall features in South Korea. When examining climatic characteristics of air temperature and rainfall by seasons, the distribution of air temperature is affected by topography and latitude for all seasons in South Korea. The distribution of rainfall demonstrated that the Yeongdong area, the southern coastal area and Jeju island have higher rainfall while the central area in Gyeongsangbuk-do is the least rainfall area. Clustering analyses of average linkage method and Ward's method was carried out using input variables derived from principal component scores calculated through Empirical Orthogonal Function analysis for air temperature and rainfall. Ward's method showed the best result of classification of climate zones. It was well reflected effects of topography, latitude, sea, the movement of surface pressure systems, and an administrative district.

Word Extraction from Table Regions in Document Images (문서 영상 내 테이블 영역에서의 단어 추출)

  • Jeong, Chang-Bu;Kim, Soo-Hyung
    • The KIPS Transactions:PartB
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    • v.12B no.4 s.100
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    • pp.369-378
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    • 2005
  • Document image is segmented and classified into text, picture, or table by a document layout analysis, and the words in table regions are significant for keyword spotting because they are more meaningful than the words in other regions. This paper proposes a method to extract words from table regions in document images. As word extraction from table regions is practically regarded extracting words from cell regions composing the table, it is necessary to extract the cell correctly. In the cell extraction module, table frame is extracted first by analyzing connected components, and then the intersection points are extracted from the table frame. We modify the false intersections using the correlation between the neighboring intersections, and extract the cells using the information of intersections. Text regions in the individual cells are located by using the connected components information that was obtained during the cell extraction module, and they are segmented into text lines by using projection profiles. Finally we divide the segmented lines into words using gap clustering and special symbol detection. The experiment performed on In table images that are extracted from Korean documents, and shows $99.16\%$ accuracy of word extraction.

Decomposition of a Text Block into Words Using Projection Profiles, Gaps and Special Symbols (투영 프로파일, GaP 및 특수 기호를 이용한 텍스트 영역의 어절 단위 분할)

  • Jeong Chang Bu;Kim Soo Hyung
    • Journal of KIISE:Software and Applications
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    • v.31 no.9
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    • pp.1121-1130
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    • 2004
  • This paper proposes a method for line and word segmentation for machine-printed text blocks. To separate a text region into the unit of lines, it analyses the horizontal projection profile and performs a recursive projection profile cut method. In the word segmentation, between-word gaps are identified by a hierarchical clustering method after finding gaps in the text line by using a connected component analysis. In addition, a special symbol detection technique is applied to find two types of special symbols tying between words using their morphologic features. An experiment with 84 text regions from English and Korean documents shows that the proposed method achieves 99.92% accuracy of word segmentation, while a commercial OCR software named Armi 6.0 Pro$^{TM}$ has 97.58% accuracy.y.

Article Analytic and Summarizing Algorithm by facilitating TF-IDF based on k-means (TF-IDF를 활용한 k-means 기반의 효율적인 대용량 기사 처리 및 요약 알고리즘)

  • Jang, Minseo;OH, Sujin;Kim, Ung-Mo
    • Proceedings of the Korea Information Processing Society Conference
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    • 2018.05a
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    • pp.271-274
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    • 2018
  • 본 논문에서는 뉴스기사 데이터를 활용하여 대규모 뉴스기사를 소주제로 분류하는 군집 분석 방법을 제안한다. 또한, 분류된 뉴스기사를 사용자가 빠르게 이해하고 접할 수 있도록 핵심 문장을 추출하여 제공하는 방법을 제안한다. 분석 데이터는 포털 사이트 점유율 1위인 네이버의 경제 분야 뉴스기사를 크롤링하여 수집한다. 뉴스기사의 분석을 위해 전 처리를 통해 특수문자, 조사, 어미, 구두점 등의 불 용어 처리를 수행한다. 또한, k-means 알고리즘을 이용하여 대용량의 뉴스기사를 주제 별로 분류하는 것을 진행하며 그것을 토대로 핵심 문장을 추출한다. 추출된 핵심 문장은 분류된 뉴스기사의 주제를 나타내며 사용자에게 빠르게 정보를 전달하기 위해 활용한다. 본 논문의 연구 내용이 여러 언론사 사이트에 반영되면 사이트 품질과 사용자 만족도 향상에 기여할 수 있을 것으로 보인다.

Discrimination System for Abusive Comments using Machine Learning (기계 학습을 이용한 악성 댓글 판별 시스템)

  • Shin, Hyo-jeong;Choi, So-Woon;Lee, Kyung-ho;Lee, Kong-Joo
    • Annual Conference on Human and Language Technology
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    • 2015.10a
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    • pp.178-180
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    • 2015
  • 본 논문에서는 기계 학습(Machine Learning)을 이용하여 댓글의 악성 여부를 분류하는 시스템에 대해 설명한다. 댓글은 문장의 길이가 짧고 맞춤법이 잘 되어있지 않는 특성을 가지고 있다. 따라서 댓글 분석을 위해 형태소 분석 결과와 문자단위 Bi-gram, Tri-gram을 자질로 이용한다. 전처리 된 댓글에서 각 자질 추출 방법에 따라 자질을 추출한다. 추출된 자질을 이용하여 기계학습 알고리즘의 모델을 학습하고 댓글의 악성 여부 분류에 활용한다. 본 논문에서는 댓글의 악성 여부 판별을 위한 자질 추출방법을 제안하고 실험을 통해 이에 대한 효용성을 검증하였다.

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