TITLE

The Notation Scanner Systems using Resilient Backpropagation Method

TYPE

Journal Article

Abstract

This research proposes anotation scanner system for numerical notation. This research was supported by using resilient backpropagation algorithm and uses the music application to get a melody of musical instruments. Objects used are designed to be focused on the numerical notation symbols. To be implemented, the input image from camera will be pre-processing, image segmentation, number and symbol recognition, output sound, and to read numerical notation symbols before entering resilient backpropagation algorithm resize the image will be 21x21 pixels. By using colour filtering can reduce errors in handwriting recognition. Success rate by using 15 new sample data with 100 sample data training, the test to get a successful outcome as much as 87.9% and 12.1% error while success rate by using 15 new sample data with 50 sample data training, the test to get a successful outcome as much as 74.4% and 25.6% error.

Citation

Christofer A, Kusuma C, Pribadi V and Budiharto D. W. (2015). The Notation Scanner Systems using Resilient Backpropagation Method. Procedia Computer Science, 59 (1), 98-105.

Keywords

resilient backpropagation, OpenCV, pattern recognition

Published On

Procedia Computer Science

Author

Ariel Christofer

  • Chandrasurya Kusuma
  • Vincent Pribadi
  • Dr. Widodo Budiharto

Copyright © BINUS UNIVERSITY. All rights reserved.