인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Given the vast range of factors, including shape, size, skew, and orientation of handwritten numerals, their machine-based recognition is a difficult challenge for researchers in the pattern recognition field. Due to the abundance of curves and resembling shapes of the symbols, the recognition of Devnagari numerals can leverage the difficulty level of the recognition. The suggested low-classification-cost method for obtaining fine features from given numeral images used benchmark deep learning models, VGG-16Net, VGG-19Net, ResNet-50, and Inception-v3, to address these issues. Principal component analysis, a powerful dimensionality reduction method, was used to efficiently reduce the number of dimensions in the information that pre-trained deep convolutional neural network models provided. The method for improving recognition accuracy by fusing features was provided in the scheme. A machine learning algorithm: support vector machine was employed for the recognition task due to its capacity to distinguish between patterns belonging to distinct classes. The system was able to obtain a recognition accuracy of 99.72% and was effective in demonstrating the importance of ensemble machine learning and deep learning approaches.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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