Abstract
In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. In this paper proposed method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor Classification (KNN) to enhance the writer identification accuracy. After feature extraction can be clustered by K-means algorithm to standardize the number of features the feature extraction and feature clustering called to gather Bag of Word (BOW), it converts arbitrary number of image feature to uniform length feature vector the proposed method experimented using (IFN/ENIT) database. The experiment result is (96.666) recognition rate.
Keywords
IFN/ENIT Database; SURF feature extraction; K-mean algorithm; KNN classifier algorithm.
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