• 제목/요약/키워드: Offline Handwriting

검색결과 5건 처리시간 0.021초

글씨쓰기 명료도 평가의 정량적 영상처리 분석 (Quantitative image processing analysis for handwriting legibility evaluation)

  • 김은빈;이초희;김은영;이언석
    • 한국산학기술학회논문지
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    • 제20권7호
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    • pp.158-165
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    • 2019
  • 선수능력의 발달 미흡과 신경학적 손상으로 인해 나타나는 쓰기 장애는 의미전달의 혼동을 줄 수 있고 가독성이 떨어지며 학습, 사회정서 문제 유발 가능성이 높다. 이에 문제 파악과 적시 개입을 위한 평가가 요구되고 있지만 임상에서는 수기에 의한 채점 방식을 채택하며 주관적인 평가에 따른 오류 가능성이 발생한다. 본 연구는 성인의 오프라인 필기체 문자를 영상처리를 통해 글자의 크기비율, 위치를 데이터화 하고 정량화 하며 수기 채점방식과의 비교, 분석을 통해 보다 객관적이고 정확하게 쓰기 수행을 평가하고자 하였다. 2018년 11월 12일부터 16일까지 신경학적 손상이 없는 성인 20명을 채택하여 10단어, 2 문장 자극을 평소 쓰기 습관을 유지한 후 연필을 사용해 따라 쓰며 쓰기 검사 데이터를 수집하였다. 본 연구에서 개발한 글씨 측정 알고리즘 결과 단어의 높이가 폭에 비해 1.2배 정도 크고 왼쪽 아래로 치우치는 경향을 보였으며 평균 9mm의 간격을 두고 띄어 썼다. Paired T test를 통한 수기와 본 시스템의 분석결과, 단어 검사와 문장 2의 검사는 고도의 상관관계를 보여 추후 검사 도구로써의 가능성을 보였다. 본 연구는 성인의 오프라인 필기체 문자를 영상처리를 통해 보다 객관적이고 정확하게 쓰기 수행을 평가하였으며 수행 규준을 위한 예비자료를 제공하였다. 향후 다양한 연령대의 쓰기 진단의 기초 자료로 제시될 수 있으며 아동의 경우 쓰기 장애 개입에 깊이 있게 활용될 수 있을 것이다.

Writer verification using feature selection based on genetic algorithm: A case study on handwritten Bangla dataset

  • Jaya Paul;Kalpita Dutta;Anasua Sarkar;Kaushik Roy;Nibaran Das
    • ETRI Journal
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    • 제46권4호
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    • pp.648-659
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    • 2024
  • Author verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.

Correction of Text Character Skeleton for Effective Trajectory Recovery

  • Vu, Hoai Nam;Na, In Seop;Kim, Soo Hyung
    • International Journal of Contents
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    • 제11권3호
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    • pp.7-13
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    • 2015
  • One of the biggest problems of skeletonization is the occurrence of distortions at the junction point of the final binary image. At the junction area, a single point usually becomes a small stroke, and the corresponding trajectory task, as well as the OCR, consequently becomes more complicated. We therefore propose an adaptive post-processing method that uses an adaptive threshold technique to correct the distortions. Our proposed method transforms the distorted segments into a single point so that they are as similar to the original image as possible, and this improves the static handwriting images after the skeletonization process. Further, we attained promising results regarding the usage of the enhanced skeletonized images in other applications, thereby proving the expediency and efficiency of the proposed method.

A Hybrid SVM-HMM Method for Handwritten Numeral Recognition

  • Kim, Eui-Chan;Kim, Sang-Woo
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2003년도 ICCAS
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    • pp.1032-1035
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    • 2003
  • The field of handwriting recognition has been researched for many years. A hybrid classifier has been proven to be able to increase the recognition rate compared with a single classifier. In this paper, we combine support vector machine (SVM) and hidden Markov model (HMM) for offline handwritten numeral recognition. To improve the performance, we extract features adapted for each classifier and propose the modified SVM decision structure. The experimental results show that the proposed method can achieve improved recognition rate for handwritten numeral recognition.

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Character Segmentation in Chinese Handwritten Text Based on Gap and Character Construction Estimation

  • Zhang, Cheng Dong;Lee, Guee-Sang
    • International Journal of Contents
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    • 제8권1호
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    • pp.39-46
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    • 2012
  • Character segmentation is a preprocessing step in many offline handwriting recognition systems. In this paper, Chinese characters are categorized into seven different structures. In each structure, the character size with the range of variations is estimated considering typical handwritten samples. The component removal and merge criteria are presented to remove punctuation symbols or to merge small components which are part of a character. Finally, the criteria for segmenting the adjacent characters concerning each other or overlapped are proposed.