• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제27권2호
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• pp.135-145
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• 2023

Recently, as deep learning technology has developed, various deep learning technologies have been introduced in handwritten recognition, greatly contributing to performance improvement. The recognition accuracy of handwritten Hangeul recognition has also improved significantly, but prior research has focused on recognizing 520 Hangul characters or 2,350 Hangul characters using SERI95 data or PE92 data. In the past, most of the expressions were possible with 2,350 Hangul characters, but as globalization progresses and information and communication technology develops, there are many cases where various foreign words need to be expressed in Hangul. In this paper, we propose a model that recognizes and combines the consonants, medial vowels, and final consonants of a Korean syllable using a multi-label classification model, and achieves a high recognition accuracy of 98.38% as a result of learning with the public data of Korean handwritten characters, PE92. In addition, this model learned only 2,350 Hangul characters, but can recognize the characters which is not included in the 2,350 Hangul characters

• 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.

• 한국산학기술학회논문지
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• 제7권5호
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• pp.887-894
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• 2006

본 논문은 런 길이를 이용해 필기체 한글 문자에서 자획의 교점을 검출하는 새로운 방법을 제안한다 이를 위해 첫째로, 수평 런 길이와 수직 런 길이를 이용해 필기체 한글 문자의 자획 두께를 구하고, 둘째로, 자획 두께를 이용해 입력 문자의 자소를 수평 성분과 수직 성분으로 분리하며, 마지막으로, 자획의 수평 성분과 수직 성분을 이용해 자획의 교점을 구하는 기술을 제안한다. 수평 성분과 수직 성분 분석은 각도와 관계없이 자획 두께와 런 길이의 변화량만을 이용해 구한다. 자획의 교점은 오프라인 필기체 한글 인식을 위한 요소 기술 중 하나인 자소 분리를 위한 분리점 후보가 되며 분리된 자획은 필기체 한글 인식을 위한 특징을 나타낸다.

• 한국IT서비스학회지
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• 제20권6호
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• pp.97-105
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• 2021

Recently, with the development of IT technology, unmanned systems are being introduced in many industrial fields, and one of the most important factors for introducing unmanned systems in the automobile field is vehicle licence plate recognition(VLPR). The existing VLPR algorithms are configured to use image processing for a specific type of license plate to divide individual areas of a character within the plate to recognize each character. However, as the number of Korean vehicle license plates increases, the law is amended, there are old-fashioned license plates, new license plates, and different types of plates are used for each type of vehicle. Therefore, it is necessary to update the VLPR system every time, which incurs costs. In this paper, we use an object detection algorithm to detect character regardless of the format of the vehicle license plate, and apply a handwritten Hangul recognition(HHR) algorithm to enhance the recognition accuracy of a single Hangul character, which is called a Hangul unit. Since Hangul unit is recognized by combining initial consonant, medial vowel and final consonant, so it is possible to use other Hangul units in addition to the 40 Hangul units used for the Korean vehicle license plate.

• Journal of information and communication convergence engineering
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• 제21권2호
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• pp.167-173
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• 2023

Hangul is unique compared to other Asian languages because of its simple letter forms that combine to create syllabic shapes. There are 24 basic letters that can be combined to form 27 additional complex letters. This produces 51 graphemes. Hangul optical character recognition has been a research topic for some time; however, handwritten Hangul recognition continues to be challenging owing to the various writing styles, slants, and cursive-like nature of the handwriting. In this study, a dataset containing thousands of samples of 51 Hangul graphemes was gathered from 110 freshmen university students to create a robust dataset with high variance for training an artificial neural network. The collected dataset included 2200 samples for each consonant grapheme and 1100 samples for each vowel grapheme. The dataset was normalized to the MNIST digits dataset, trained in three neural networks, and the obtained results were compared.

• 인지과학
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• 제7권4호
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• pp.63-74
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• 1996

• 한국산학기술학회논문지
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• 제7권3호
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• pp.385-392
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• 2006

본 논문은 오프라인 필기체 한글 인식을 위한 요소 기술의 하나인 자소 분할을 위한 새로운 자획 추출법을 제안한다. 수평 런 길이를 이용하여 자소의 자획을 수직, 경사, 수평으로 구분 분리한다. 수직 자획이나 경사 자획의 수평 런 길이는 자획 두에가 되며, 수평 자획의 수평 런의 개수가 자획 두께가 된다. 수평 자획을 분리 추출한 후, 끊어진 수직, 경사 자획을 자획 두께의 수평 런으로 연결하여 분리한 자획들이 문자의 특징을 나타내게 한다. 추출된 자획들은 온라인 필기체 한글 인식 시스템에서 개발 사용되고 있는 자획 사전 정합을 통해 문자 인식을 할 수 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.1019-1022
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• 1999

In this paper, we analysis the effect of a dictionary in a handwritten Hangul word recognition problem in terms of its size and the length of the words in it. With our experimental results, we can account for the word recognition rate depending not only on character recognition performance, but also much on the amount of the information that the dictionary contains, as well as the reduction rate of a dictionary.

• 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.

• 한국산업정보학회논문지
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• 제5권1호
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• pp.25-30
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• 2000

한글은 부류수의 방대성과 글자간의 유사성으로 인해 인식이 어려운 문자 집합으로 간주되고 있다. 기존 연구 대부분은 일반적으로 사용되는 2,350 글자를 대상으로 인식을 시도하였는데, 이는 일반성을 제공하는 대신 낮은 성능 문제를 안고 있다. 이에 반해, 우편 영상이나 전표 영상 등의 특정 필드에 나타나는 한글만을 대상으로 하는 접근 방법이 보다 현실적이라 할 수 있다. 본 논문은 금액란에 나타나는 필기 한글을 인식하는 연구를 기술한다. 인식을 위해 모듈러 신경망 인식기를 사용하였으며, 세 종류의 특징을 사용하였다. 표준 한글 데이터베이스 PE92에 대해 실험한 결과 정인식률 97.56%를 얻었다.