Numeral recognition using curvelet transform

This paper proposes the performance of two new algorithms for digit recognition. These recognition systems are based on extracted features on the performance of image's curvelet transform & achieving standard deviation and entropy of curvelet coefficients matrix in different scales & various angels. In addition, the proposed recognition systems are obtained by using different scales information as feature vector. So, we could clarify the most important scales in aspect of having useful information .Finally by employing the Knn classifier we classify them into predefined classes. The classifier was trained and test with handwritten numeral database, MNIST The results of this test shows, that our correct recognition rate in "curvelet transform+ standard deviation" algorithm is 93% and in "curvelet transform+ entropy" algorithm is 82%.