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Off-line recognition of text plays a significant role in several applications such as the automatic sorting of postal mail or editing old documents. The recognition of Arabic handwriting characters is a difficult task owing to the similar appearance of some different characters. Most researchers have presented methods that recognise isolated characters. However, recognition of all shapes of Arabic handwritten characters still remains a great challenge. The selection of the methods for feature extraction and classification remain the most important step in achieving high recognition accuracy. The purpose of this paper is to compare the effectiveness of DCT and DWT in capturing discriminative features of all shapes of Arabic handwritten characters including overlapping characters with ANN and HMM in the classification stage. Since, the recognition of handwritten characters is an important step in the recognition of a word after segmentation, this paper ascertains the effectiveness of these techniques in capturing useful information and, hence, achieving more accurate recognition results. This work has been tested with HACDB database containing 6,600 shapes of Arabic characters. The results have demonstrated that the feature extraction by DCT with ANN yields a higher recognition rate.