A novel electromyography (EMG) based classification approach for Arabic handwriting
Azzedine Lansari, Faouzi Bouslama, Mohammad Ali Khasawneh, Azmi Tawfeq Hussein Alrawi · 2004
In this paper, a novel classification approach for handwritten Arabic characters is proposed. Features for classification are extracted from electromyographic (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.