A holistic approach for Off-line handwritten cursive word recognition using directional feature based on Arnold transform

We present an off-line handwritten word recognition system.The system is based on extraction of directional features.Arnold transform followed by Hough transform is used to extract directional feature.A multi-class linear SVM is employed to recognize handwritten cursive words.Experiments are done on CENPERMI database of legal amounts written in English. This paper presents a holistic off-line handwriting recognition system based on extraction of directional features which depends on the stroke orientation distribution of cursive word. This stroke orientation distribution is estimated using Arnold transform followed by Hough transform. Besides this feature some other directional shape features are also used to form feature vector. Finally, a multi-class linear SVM is employed to recognize cursive word. Experiments are carried out on CENPARMI database of legal amount written in English and an overall accuracy of 87.19% is achieved. We have also compared our proposed method with the state-of-the-art methods for handwritten character recognition using C-Cube data-set.

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