• 전자공학회논문지B
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• 제31B권4호
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• pp.1-8
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• 1994

In this paper, we propose an efficient two-step pattern matching method which promises shape distortion-tolerant recognition of handwritten of handwritten Hangul syllables. In the first step, nonlinear shape normalization is carried out to compensate for global shape distortions in handwritten characters, then a preliminary classification based on simple pattern matching is performed. In the next step, nonlinear pattern matching which achieves best matching between input and reference pattern is carried out to compensate for local shape distortions, then detailed classification which determines the final result of classification is performed. As the performance of recognition systems based on pattern matching methods is greatly effected by the quality of reference patterns. we construct reference patterns by combining the proposed nonlinear pattern matching method with a well-known averaging techniques. Experimental results reveal that recognition performance is greatly improved by the proposed two-step pattern matching method and the reference pattern construction scheme.

• 전자공학회논문지B
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• 제31B권9호
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• pp.85-100
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• 1994

This paper proposes a recognition system of constrained handwritten Hangul and alphanumeric characters using discrete hidden Markov models (HMM). HMM process encodes the distortion and similarity among patterns of a class through a doubly stochastic approach. Characterizing the statistical properties of characters using selected features, a recognition system can be implemented by absorbing possible variations in the form. Hangul shapes are classified into six types by fuzzy inference, and their recognition is performed based on quantized features by optimally ordering features according to their effectiveness in each class. The constrained alphanumerics recognition is also performed using the same features used in Hangul recognition. The forward-backward, Viterbi, and Baum-Welch reestimation algorithms are used for training and recognition of handwritten Hangul and alphanumeric characters. Simulation result shows that the proposed method recognizes handwritten Korean characters and alphanumerics effectively.

• 한국정보통신학회논문지
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• 제6권6호
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• pp.986-991
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• 2002

본 논문에서는 고유벡터를 이용한 오프라인 필기체 숫자인식 기법을 제시한다. 본 기법에서는 KL 변환에 의한 고유벡터를 이용하여 통계적으로 숫자의 특징을 추출하며, 특징공간상에서 최소거리기법으로 숫자를 인식한다. 본 기법에서 제안된 특징추출 방법에서는 많은 표본 숫자영상에서 각 숫자들의 특징을 가장 잘 표현하는 기저벡터를 찾아내고 이로부터 숫자의 특징을 구한다. 제시된 기법의 성능 평가를 위해 Concordia대학의 무제약 필기체 숫자 데이터베이스를 사용하여 실험한 결과 96.2%의 인식률을 얻을 수 있었다.

• 전자공학회논문지B
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• 제29B권9호
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• pp.8-16
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• 1992

This paper presents a method which can recognized the Handwritten Korean characters by using a Context-Free Grammar. The input characters are thinned in order to dwindle the mount of data, the thinned characters are converted into one-dimension strings according to six-forms. when the point of contact among phonemes is found, two phonemes are seperated respectively by marking the index mark (\) at the points. The Context-Free Grammar to input characters is classified into group grammars concerning the similarity of phonemes, input characters are parsed by making use of the Pushdown automata method. As the bent parts in the Handwritten characters are found frequently, We try to correct the bent parts by using the parsing distance measure, which recognize characters according to minium value caused by measuring the weight distance between two sentences. In this experiment, the recognition rate shows 93.8% to 275 Handwritten Korean characters.

• 스마트미디어저널
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• 제4권3호
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• pp.44-49
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• 2015

Text line segmentation is a preprocessing step in OCR, which can significantly influence the accuracy of document analysis applications. This paper proposes a novel methodology for the text line segmentation of handwritten documents. First, the average width of the connected components is used to form a 1-D Gaussian kernel and a smoothing operation is then applied to the input binary image. The adaptive binarization of the smoothed image forms the final text lines. In this work, the segmentation method involves two stages: firstly, the large connected components are labelled as a unique text line using text line area mapping. Secondly, the final refinement of the segmentation is performed using the Euclidean distance between the text line and small connected components. The group of uniquely labelled text candidates achieves promising segmentation results. The proposed approach works well on Korean and English language handwritten documents captured using a camera.

• 융합신호처리학회논문지
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• 제3권1호
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• pp.48-53
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• 2002

본 논문에서는 무제약 필기체 숫자 인식에 있어서 향상된 RBF(Radial Basis Function) 신경망을 이용한 새로운 접근 방법을 제시하였다. RBF 신경망은 인식률과 인식 속도를 향상시키기 위해 기저 함수로서 Raised Cosine RBF를 사용하였다. Raised Cosine RBF 신경망 분류기의 성능 평가를 위하여 캐나다 몬트리올 Concordia 대학의 무제약 필기체 숫자 데이터베이스를 사용하였고, 실험 결과 98.05%의 인식률을 보였다.

• 스마트미디어저널
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• 제5권3호
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• pp.30-34
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• 2016

Nowadays, there are a lot of Korean documents, which often need to be identified in one of printed or handwritten text. Early methods for the identification use structural features, which can be simple and easy to apply to text of a specific font, but its performance depends on the font type and characteristics of the text. Recently, the bag-of-words model has been used for the identification, which can be invariant to changes in font size, distortions or modifications to the text. The method based on bag-of-words model includes three steps: word segmentation using connected component grouping, feature extraction, and finally classification using SVM(Support Vector Machine). In this paper, bag-of-words model based method is proposed using SURF(Speeded Up Robust Feature) for the identification of machine printed and handwritten text in Korean documents. The experiment shows that the proposed method outperforms methods based on structural features.

• Journal of Information Processing Systems
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• 제11권3호
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• pp.325-341
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• 2015

This paper describes the Tezpur University dataset of online handwritten Assamese characters. The online data acquisition process involves the capturing of data as the text is written on a digitizer with an electronic pen. A sensor picks up the pen-tip movements, as well as pen-up/pen-down switching. The dataset contains 8,235 isolated online handwritten Assamese characters. Preliminary results on the classification of online handwritten Assamese characters using the above dataset are presented in this paper. The use of the support vector machine classifier and the classification accuracy for three different feature vectors are explored in our research.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 하계학술대회
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• pp.84-87
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• 2018

Handwritten letter recognition is important for numerous real-world applications and many topics like human-machine interaction, education, entertainment, and more. This paper describes the implementation of a real-time handwritten letters recognition system on a common embedded computer. Recognition is performed using a customized convolutional neural network, which was designed to work with low computational resources such as the Raspberry Pi platform. The experimental results show that the proposed real-time system achieves an outstanding performance in the accuracy rate and the response time for recognition of twenty-six handwritten letters.

• 한국정보통신학회논문지
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• 제26권7호
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• pp.1098-1101
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• 2022

With the development of computer music notation programs, when drawing sheet music, it is often drawn using a computer. However, there are still many use of hand-written notations for educational purposes or to quickly draw sheet music such as listening and dictating. In previous studies, OMR focused on recognizing the printed music sheet made by music notation program. the result of handwritten OMR with camera is poor because different people have different writing methods, and lens distortion. In this study, as a pre-processing process for recognizing handwritten music sheet, we propose a method for recognizing a staff using linear regression and a method for recognizing a bar using CNN. F₁ scores of staff recognition and barline detection are 99.09% and 95.48%, respectively. This methodologies are expected to contribute to improving the accuracy of handwriting.