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This paper presents the results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques. The tested databases are obtained from MNIST [1] and collected samples of digits handwritten by teachers at Da Nang University of Technology. For feature extraction, two features are chosen: Hu’s seven moments and image averaging (resizing the images to ones of less number of pixels for easier comparison). The preceding features are accompanied with corresponding classifiers, which are Neural Network classifier and Euclidean Distance. So far with the dictionary for matching collected at Da Nang University of Technology, the combination. | Tuyển tập Báo cáo Hội nghị Sinh viên Nghiên cứu Khoa học lần thứ 8 Đại học Đà Nẵng năm 2012 HANDWRITTEN NUMBER RECOGNITION AND ITS APPLICATION AT DANANG UNIVERSITY OF TECHNOLOGY Authors Duong Thi Kim Cuc Dinh Quang Huy Tran Hoang An Nguyen Van Trong Da Nang University of Technology Center of Excellence ECE08 Advisors Hoang Le Uyen Thuc M.S. Pham Van Tuan Ph.D. Da Nang University of Technology Department of Electronics and Telecommunications ABSTRACT This paper presents the results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques. The tested databases are obtained from MNIST 1 and collected samples of digits handwritten by teachers at Da Nang University of Technology. For feature extraction two features are chosen Hu s seven moments and image averaging resizing the images to ones of less number of pixels for easier comparison . The preceding features are accompanied with corresponding classifiers which are Neural Network classifier and Euclidean Distance. So far with the dictionary for matching collected at Da Nang University of Technology the combination of image averaging feature and the Euclidean Distance gives the best accuracies more than 93 and can further be improved with a more comprehensive database. 1. Introduction One of the most troublesome and tedious tasks teachers at Da Nang University of Technology generally face is to manually put the exam grades into computers. This project aims at providing them with the convenience of not having to copy the grades by hands by presenting a method of automatically importing grades into computers. This technique employs a well-known procedure in pattern recognition called OCR optical character recognition . The performance of character recognition largely depends on the feature extraction approach and the classification learning scheme. For feature extraction of character recognition various approaches have been proposed. Hu s .
