Real time object detection using Hopfield neural network for Arabic printed letter recognition

In this work, a new technique of improving Hopfield model for object edge detection of Arabic letters recognition is proposed. In conventional methods, different trends for object segmentation are used to split cursive letters individually for recognition. The presented technique differentiates only letters with no maintain of background data. Each letter is a set of clustered small weights distributed according to its shape within the word. The average of Total Letter Weight is a special property for each form of the letters. Preliminary experimental tests show positive performance of the proposed system.

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