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