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Title(KR)
Unity3D에서 딥러닝을 이용한 필기체 인식 개발
Title(ENG)
Development of Handwriting Recognition Using Deep Learning in Unit3yD
Keywords(KR)
Character recognition, Deep learning, Unity3D,Handwriting Recognition, Convolutional Neural Network
Keywords(ENG)
Character recognition, Deep learning, Unity3D,Handwriting Recognition, Convolutional Neural Network
Author
Hye Moon Lee, Won Hyung Lee
Abstract(KR)필기 인식은 사람이 작성한 문서나 종이에 쓴 글자, 사진에 보이는 글자 등을 인식하는 기술이다. 대표적인 기술로는 OCR과 온라인 필기인식 기술이 있으며 OCR은 정자로 또박또박 쓴 글씨 인식률은 높지만 그렇지 않는 경우에는 인식률이 낮다. 온라인 필기인식 기술은 필기 입력순서와 사람의 필체의 차이에 따라 인식률이 확연하게 달랐다. 본 논문에서는 이러한 단점을 보완하고자 딥러닝을 이용하여 필기체 인식 시스템을 제안하 고자 한다. 본 논문에서는 신경망 알고리즘 중 Convolutional Neural Network와 EMNIST 데이터 세트를 사용 하여 학습 데이터를 설계하였고 Unity3D 게임엔진을 이용하여 전체적인 시스템을 구성하였다. 또한 본 논문에 서는 CPU와 GPU 성능이 학습 결과에 영향을 미치는지 알아보기 위해 성능을 비교분석을 하였고, loss 값과 accuracy 결과에 큰 차이는 없었지만 학습 속도에는 최대 30배 정도 속도 차이가 났다. 마지막으로 실험을 통 해 시스템 인식결과를 분석하였고, 문자와 숫자가 유사한 O, q, l과 같은 알파벳이나, 실험자가 글자를 다른 알파벳과 유사하게 보이게 필기하면 인식률이 낮았다. 본 논문에서 제안하는 시스템은 게임엔진을 사용하여 인공지능 시스템을 개발했기 때문에 프로세스 절차가 간략해졌고 호환성도 좋아졌다.
Abstract(ENG)Handwriting recognition is a technology that recognizes people's written documents, characters written on paper, and characters shown in pictures. Typical technologies include OCR and on-line handwriting recognition technology. OCR had a high recognition rate of writing that was clearly written, but if not, it had a low recognition rate. On-Line handwriting recognition technology clearly differed depending on the difference between handwriting input order and a person's handwriting. In this paper, in order to compensate for these shortcomings, we propose a handwriting recognition system using deep learning. In this paper, the learning data were designed using Convolutional Neural Network and EMNIST data set among neural network algorithms and the whole system was constructed using Unity3D game engine. In this paper, we also did a comparative analysis to see if CPU and GPU performance affect learning results, and although there was no big difference in loss value and acuracy value results, there was a maximum speed difference of 30 times at learning speed. Finally, the results of recognition were analyzed through experiments, and alphabets with similar shapes of characters and numbers, such as O, q, and l, had lower recognition rates. And if the experimenter wrote the characters to look similar to other alphabets, the recognition rate was low. The system proposed in this paper has developed an artificial intelligence system using game engine, so the process procedure has been simplified and the compatibility has improved.
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