Recognition of Bangla Handwritten Characters Using an MLP Classifier Based on Stroke Features
A recognition scheme for handwritten basic Bangla (an Indian script) characters is proposed. No such work has been reported before on a reasonably large representative database. Here a moderately large database of Bangla handwritten character images is used for the recognition purpose. A handwritten character is composed of several strokes whose characteristics depend on the handwriting style. The strokes present in a character image are identified in a simple fashion and 10 certain features are extracted from each of them. These stroke features are concatenated in an appropriate order to form the feature vector of a character image on the basis of which an MLP classifier is trained using a variant of the backpropagation algorithm that uses self-adaptive learning rates. The training and test sets consist respectively of 350 and 90 sample images for each of 50 Bangla basic characters. A separate validation set is used for termination of training of the MLP.
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