A Novel Lexicon Reduction Method for Arabic Handwriting Recognition

In this paper, we present a method for lexicon size reduction which can be used as an important pre-processing for an off-line Arabic word recognition. The method involves extraction of the dot descriptors and PAWs (Piece of Arabic Word ). Then the number and position of dots and the number of the PAWs are used to eliminate unlikely candidates. The extraction of the dot descriptors is based on defined rules followed by a convolutional neural network for verification. The reduction algorithm makes use of the combination of two features with a dynamic matching scheme. On IFN/ENIT database of 26459 Arabic handwritten word images we achieved a reduction rate of 87% with accuracy above 93%.