A Novel Electromyography (EMG) Based Classification Approach for Arabic Handwriting
Source of Publication
Proceedings of the International Joint Conference on Neural Networks
In this paper, a novel classification approach for handwritten Arabic characters is proposed. Features for classification are extracted from electromygraphic (EMG) signals detected on two forearm muscles. Noise cancellations in conjunction with a process parameter estimator for feature identification are proposed. Neural networks using a potentially damped least mean squared algorithm is used at the classification stage. The proposed new classification technique is used on handwritten Arabic characters.
Algorithms; Feature extraction; Muscle; Neural networks; Neural disorders; Electromyography
Lansari, Azzedine; Bouslama, Faouzi; Khasawneh, Mohammed; and Al-Rawi, Akram, "A Novel Electromyography (EMG) Based Classification Approach for Arabic Handwriting" (2003). All Works. 199.
Indexed in Scopus