필기체 인식을 위한 컨벌루션 신경망 모델

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 장병탁.모바일 기기가 대중화 되면서 필기체 인식은 더욱 중요해졌고, 이와 관련해 필기체 인식의 성능을 높이기 위한 다양한 방법이 연구되고 있다. 필기체 인식의 성능을 높이기 위한 방법으로는 주로 인공 신경망을 이용한 연구가 활발히 이루어졌는데, 그 중에서도 컨벌루션 신경망을 이용한 연구가 좋은 결과를 보여주었다. 컨벌루션 신경망은 심층 학습의 일종으로 커널이라는 필터를 이용해 학습 데이터에 다양한 변화를 주는데, 본 논문에서는 이러한 특징을 앙상블 기법과 연관시켜 보았다. 또한 컨벌루션 신경망에 대해 많은 연구가 이루어지지 않은 모델 복잡도에 대해 실험해보며 기존의 신경망과 어떤 차이점이 있는지를 알아보았다. 컨벌루션 신경망의 모델 복잡도에 대해 알아보기 위해 계층의 수, 학습 횟수, 파라미터의 수 등을 변화시키며 모델 복잡도와 컨벌루션 신경망의 성능의 관계를 알아보았고, 기존 신경망에 비해 견고한 특징이 있는 것을 확인하였다.1. 서론 ······························································································· 1 1.1 연구배경 ·········································································· 1 1.2 관련연구 ·········································································· 2 2. 컨벌루션 신경망 ········································································· 3 2.1 배경 ·················································································· 3 2.2 컨벌루션 계층 ································································· 8 2.2.1 커널 ···································································· 8 2.2.2 가중치 공유 ······················································· 9 2.3 서브 샘플링 계층 ·························································· 10 3. 앙상블 기법 ················································································ 10 4. 실험 방법 및 결과 ····································································· 14 4.1 실험 데이터 ··································································· 14 4.2 실험 내용 ······································································· 14 4.3 결과 분석 ······································································· 19 5. 결론 및 향후 연구 ····································································· 20Maste