• 제목/요약/키워드: handwritten

검색결과 354건 처리시간 0.028초

Online Digit Recognition using Start and End Point

  • Shim, Jae-chang;Ansari, Md Israfil
    • Journal of Multimedia Information System
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    • 제4권1호
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    • pp.39-42
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    • 2017
  • Communication between human and machine is having been researched from last few decades and still it's a challenging task because human behavior is unpredictable. When it comes on handwritten digits almost each human has their own writing style. Handwritten digit recognition plays an important role, especially in the courtesy amounts on bank checks, postal code on mail address etc. In our study, we proposed an efficient feature extraction system for recognizing single digit number drawn by mouse or by a finger on a screen. Our proposed method combines basic image processing and reading the strokes of a line drawn. It is very simple and easy to implement in various platform as compare to the system which required high system configuration. This system has been designed, implemented, and tested successfully.

축합조건의 분석을 통한 Langevine 경쟁 학습 신경회로망의 대역 최소화 근사 해석과 필기체 숫자 인식에 관한 연구 (A study of global minimization analaysis of Langevine competitive learning neural network based on constraction condition and its application to recognition for the handwritten numeral)

  • 석진욱;조성원;최경삼
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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    • pp.466-469
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    • 1996
  • In this paper, we present the global minimization condition by an informal analysis of the Langevine competitive learning neural network. From the viewpoint of the stochastic process, it is important that competitive learning guarantees an optimal solution for pattern recognition. By analysis of the Fokker-Plank equation for the proposed neural network, we show that if an energy function has a special pseudo-convexity, Langevine competitive learning can find the global minima. Experimental results for pattern recognition of handwritten numeral data indicate the superiority of the proposed algorithm.

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터치스크린을 이용한 필기체 문자 인식 알고리즘 설계 및 구현 (Implementation and Design of Handwritten Character Recognition Algorithm Using Touch Screen)

  • 박상봉
    • 한국인터넷방송통신학회논문지
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    • 제14권2호
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    • pp.141-146
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    • 2014
  • 본 논문은 모바일 터치스크린을 이용한 필기체 문자 인식 알고리즘을 제안하고, 구현된 내용을 기술한다. 제안된 시스템은 PXA320 프로세서, 정전 용량 터치 패널과 QT4를 이용한 인터페이스로 구성하였다. C++ 언어를 사용하고 제안된 알고리즘은 문자의 특성을 직선, 좌호, 우호 특징을 추출하여 3진 트리 방식으로 입력되는 문자를 결정한다. 영문자에 대한 테스트를 통하여 성능을 검증하였다. 기존 방식보다 간단한 알고리즘으로 구성되므로, 모바일 터치 스크린의 문자인식에 적용이 가능하다.

부분 투영기법을 이용한 필기체 주소 영상에서의 문자열 분리 (Text line separation in handwritten address image using partial projection technique)

  • 정선화;남윤석
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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    • pp.31-34
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    • 2003
  • In this paper, we describe a method for separating text lines in handwritten Korean address images. The most remarkable feature of the proposed method is to use a modified projection technique. named a partial projection technique. A projection based text line separation method which projects the whole address image in horizontal direction to find split points for text line separation cannot avoid failing separation in case of images with a little skew or overlap between vertically neighboring text lines. To overcome this problem, we have introduced a partial projection technique which splits an address image into a few partial address images to be equal width and then project them each horizontally. The experiment done with 989 handwritten Korean address images extracted from live mails shows the superiority of the proposed method. The correct text-line separation rate fir the testing images was about 91.5%.

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필기체 숫자인식을 위한 병렬 자구성 계층 신경회로망 (Parallel, self-organizing, hierarchical neural networks for handwritten digit recognition)

  • 방극준;조남신;강창언;홍대식
    • 전자공학회논문지B
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    • 제33B권7호
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    • pp.173-182
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    • 1996
  • In this paper, we propose the parallel, self-organizing, hierarchical neural netowrks as a handwritten digit recognition system. This system can absorb the various shape variations of handwritten digits by using the different methods of extracting the features in each stage neural network (SNN) of the PSHNN, and can reduce training time by using the single layer neural network as the SNN, and can obtain high rate of correct recognition by using the certainty area in all the output nodes individually. experiments have been performed with NIST database. In which we use 21, 315 digits (10, 625 digits for training and 10,663 digits for testing). The results show that the correct rate is 97.48% the error rate is 1.72% and the reject rate is 0.78%.

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속성문법을 이용한 필기체 한글 문서 내의 자모인식 (The Recognition of Vowels and Consonants in a Handwritten Hangul Text with Attributed Grammars)

  • 유승필;김태균
    • 대한전자공학회논문지
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    • 제26권3호
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    • pp.85-94
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    • 1989
  • 글자의 간격과 크기가 일정하지 않으므로 전처리 과정에서 각 글자를 분리하기 어려운 필기체 한글 문서로 부터 자모들을 인식하는 방법을 제안한다. 본 방법은 세선화된 필기체 한글문서의 영상 내에 있는 모든 글자들을 스트로크들로 변환시키고, 이들 사이의 배열관계를 나타내는 속성을 추출한 다음, 이들 스트로크와 속성들에 대해 속성문법을 적용하여 자모들을 인식한다.

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Robust Stroke Extraction Method for Handwritten Korean Characters

  • Park, Young-Kyoo;Rhee, Sang-Burm
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2000년도 ITC-CSCC -2
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    • pp.819-822
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    • 2000
  • The merit of the stroke extraction algorithm is the ease of the feature abstraction from the skeleton of a character, But, extracting strokes from Korean characters has two major problems that must be dealt with. One is extracting primitive strokes and the other is merging or splitting the strokes using dynamic information of the strokes. In this paper, a method is proposed to extract strokes from an off-line handwritten Korean character. We have developed some stroke segmentation rules based on splitting, merging and directional analysis. Using these techniques, we can extract and trace the strokes in an off-line handwritten Korean character accurately and efficiently.

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Offline Handwritten Numeral Recognition Using Multiple Features and SVM classifier

  • Kim, Gab-Soon;Park, Joong-Jo
    • 전기전자학회논문지
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    • 제19권4호
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    • pp.526-534
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    • 2015
  • In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by local shrinking and expanding operations, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where the concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our scheme is tested by recognition experiments on the handwritten numeral database CENPARMI, where SVM classifier with RBF kernel is used. The experimental results show the usefulness of our scheme and recognition rate of 99.10% is achieved.

필기체 문자 인식을 위한 문자 영상 데이터 구축에 관한 연구 (A Study of Construction of Character Image Data for Recognition Handwritten Text)

  • 이향란;고경철;이말례
    • 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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    • 한국정보과학회언어공학연구회 2000년도 제12회 한글 및 한국어 정보처리 학술대회
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    • pp.63-67
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    • 2000
  • In order to develop a character recognition system, it is an essential preceding work that gathers an image data of the standard. On this purpose a data of the digitized images of a handwritten characters was collected. The types of a gathered image data are Korean character, Chiness character, Numeral, English character, Special character, and so on. This paper deals with a handwritten character image data base, and the image data base different from the general storage structure of a lame capacity multimedia was designed and builded.

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A Fuzzy Genetic Classifier for Recognition of Confusing Handwritten Numerals 4,6, and 9

  • Shin, Dae-Jung;Na, Seung-You;Kim, Sun-Hee
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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    • pp.11-14
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    • 1995
  • A Fuzzy Classifier which deals with very confusing objects is proposed. Naturally this classifier heavily relies on the nulti-feature decision-making procedure. For a simple example, this classifier is applied to the recognition of confusing handwritten numerals 4,6 and 9 The characteristic variables used in this paper are the existence of a loop and the relative location of the starting or ending points(SEP). Thus each sample of handwritten numerals 4, 6 and 9 is classified in one of the 6 groups which are divided according to the sample structure. Each group has its own classifying rules. Also the method of rule-generation using genetic algorithms in each group is proposed.

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