• International Journal of Contents
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• v.8 no.1
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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.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.571-573
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• 2002

음성인식과 온라인 필기인식에서 우수한 성능을 보이는 은닉 마르코프(HMM)의 HMM의 구조는 휴리스틱 한 방법에 의해 결정되는 것이 일반적이기 때문에 최적의 모델을 선택하는데 어려움이 있다. 이에 본 논문에서는 HMM의 구조를 체계적인 방법으로 정함과 동시에 변별력의 단점을 개선 할 수 있는 방법으로 Anti-likelihood를 이용한 모델간의 변별력을 살펴보고 최적의 모델 선택 기준인 BIC와의 결합하여, 체계적이고 효율적인 최적 모델 선택이 가능한 방법론에 대해 연구하고 필기데이터에 대해 검증한 결과, 기존의 방법보다 파라미터의 수는 감소되고 인식률이 향상됨을 알 수 있다.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.285-288
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• 2004

온라인 필기체 한자는 동일인의 동일 문자조차도 회수, 획순 및 형태의 변화가 다양할 뿐만 아니라 인식 대상이 방대하여 인식이 매우 어렵다. 또한 한자는 기본 자소의 조합에 의한 글자가 아닌 각각의 글자가 독립적으로 이루어져 있어 연속된 획들 간의 관련도를 파악하기 어렵고 획수도 1획에서 28획까지 다양하게 분포를 한다. 따라서 본 연구에서는 대분류 단계로 시작획 비교를 하고 이어진 세분류 단계에서 문자의 특징으로 방향코드와 특이점을 추출해내고 획수를 고려하여 DP 정합을 하는 2단계 인식 시스템을 제안하였다. 이로써 최적의 속도로 입력한 문자를 찾아낼 수 있도록 하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.97-107
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• 2002

In this paper, we propose the realization of on-line handwritten Chinese character recognition for mobile personal digital assistants (PDA). We focus on the development of an algorithm having a high recognition performance under the restriction that PDA requires small memory storage and less computational complexity in comparison with PC. Therefore, we use index matching method having computational advantage for fast recognition and we suggest a unit reconstruction method to minimize the memory size to store the character models and to accomodate the various changes in stroke order and stroke number of each person in handwriting Chinese characters. We set up standard model consisting of 1800 characters using a set of pre-defined units. Input data are measured by similarity among candidate characters selected on the basis of stroke numbers and region features after preprocessing and feature extracting. We consider 1800 Chinese characters adopted in the middle and high school in Korea. We take character sets of five person, written in printed style, irrespective of stroke ordering and stroke numbers. As experimental results, we obtained an average recognition time of 0.16 second per character and the successful recognition rate of 94.3% with MIPS R4000 CPU in PDA.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.807-815
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• 2008

A simple efficient method of spotting and recognizing hand gestures in video is presented using a network of hidden Markov models and dynamic programming search algorithm. The description starts from designing a set of isolated trajectory models which are stochastic and robust enough to characterize highly variable patterns like human motion, handwriting, and speech. Those models are interconnected to form a single big network termed a spotting network or a spotter that models a continuous stream of gestures and non-gestures as well. The inference over the model is based on dynamic programming. The proposed model is highly efficient and can readily be extended to a variety of recurrent pattern recognition tasks. The test result without any engineering has shown the potential for practical application. At the end of the paper we add some related experimental result that has been obtained using a different model - dynamic Bayesian network - which is also a type of stochastic model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.185-196
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• 1999

In this dissertation, a new off-line recognition system is proposed using a subgraph pattern, neural network. After thinning is applied to input characters, balance having a noise elimination function on location is performed. Then as the first step for recognition procedure, circular elements are extracted and recognized. From the subblock HT, space feature points such as endpoint, flex point, bridge point are extracted and a subgraph pattern is formed observing the relations among them. A region where vowel can exist is allocated and a candidate point of the vowel is extracted. Then, using the subgraph pattern dictionary, a vowel is recognized. A same method is applied to extract horizontal vowels and the vowel is recognized through a simple structural analysis. For verification of recognition subgraph in this paper, experiments are done with the most frequently used Myngjo font, Gothic font for printed characters and handwritten characters. In case of Gothic font, character recognition rate was 98.9%. For Myngjo font characters, the recognition rate was 98.2%. For handwritten characters, the recognition rate was 92.5%. The total recognition rate was 94.8% with mixed handwriting and printing characters for multi-font recognition.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.4
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• pp.54-64
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• 1999

This paper proposes an efficient handwritten English word recognition scheme which can be applied practical applications. To effectively use the lexicon which is available in most handwriting related applications, the lexicon entries are introduced in the early stage of the recognition. Dynamic programming is used for matching between over-segmented character segments and letters in the lexicon entries. Character segmentation statistics which can be obtained while the training is being performed are used to adjust the matching window size. Also, the matching results between the character segments and the letters in the lexicon entries are cached to avoid repeat of the same computation. In order to verify the effectiveness of the proposed methods, several experiments were performed using thousands of word images with various writing styles. The results show that the proposed methods significantly improve the matching speed as well as the accuracy.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.49-56
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• 2020

The virtual Korean keyboard system is a method of inputting characters by touching a fixed position. This system is very inconvenient for people who have difficulty moving their fingers. To alleviate this problem, this paper proposes an efficient framework that enables keyboard input and handwriting through video and user motion obtained through the RGB camera of the mobile device. To develop this system, we use face recognition to calculate control coordinates from the input video, and develop an interface that can input and combine Hangul using this coordinate value. The control position calculated based on face recognition acts as a pointer to select and transfer the letters on the keyboard, and finally combines the transmitted letters to integrate them to perform the Hangul keyboard function. The result of this paper is an efficient writing system that utilizes face recognition technology, and using this system is expected to improve the communication and special education environment for people with physical disabilities as well as the general public.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.178-185
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• 2009

A signal processing algorithm is proposed for end point detection which is used commonly in accelerometers-based pattern recognition problem. In the conventional method, end points are detected by manual manipulation with an additive button or algorithm based on statistical computation and highpass filtering to cause critical time delay and difficulty for parameters optimization. As an solution, we propose a focused Time Lagged Feedforward Network(TLFN) with respect to a differential signal of acceleration, which is widely applied for time series forecasting. The simple experiment is conducted with handwriting and the detection performance and response characteristic of the proposed algorithm is tested and analyzed.