Topic
Hartley transform
About: Hartley transform is a research topic. Over the lifetime, 2709 publications have been published within this topic receiving 79944 citations.
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TL;DR: Experimental results have shown that the Hartley descriptors are comparable to the FFT-based Fourier descriptors and better than the MFS descriptors in terms of recognition rate.
Abstract: Fast Hartley transform (FHT) is an integral transform which shares some features with the Fourier transform. Fourier transform is used successfully in computing the Fourier descriptors which are used in the recognition of characters and objects. In this paper, printed Arabic optical character recognition using Hartley transform is presented. The Hartley descriptors are estimated by applying the FHT to the Arabic printed characters. The contour of the Arabic character primary part is extracted and then FHT is applied to the extracted contours. Hartley features are extracted from the FHT domain. These features are used for the recognition of Arabic characters. It was experimentally proven that the use of 10–20 descriptors gives the best recognition rate. Hence, ten descriptors were used to save computation and processing times. Experimental results using ten Hartley descriptors resulted in a recognition rate of 97% and an error rate of 3%. Arabic characters’ dots and holes were used in addition to the ten Hartley descriptors to enhance the recognition rate. The use of these features resulted in a 97.3 recognition rate, 2% rejection rate, and 0.7% error rate. The dot feature was also used to reduce the number of classes of the Arabic characters without affecting the recognition rate or the number of recognized characters. This technique, based on Hartley descriptors, was compared with the Fourier descriptors calculated from the fast Fourier transform (FFT) and with modified Fourier spectrum (MFS) descriptors. Experimental results have shown that the Hartley descriptors are comparable to the FFT-based Fourier descriptors in terms of recognition rate. The Hartley and FFT-based descriptors are better than the MFS descriptors in terms of recognition rate.
9 citations
TL;DR: In this article, a finite field fast convolutional transform using the Chinese remainder theorem is proposed. But it requires only real arithmetic (addition and multiplication) to compute the transform.
Abstract: The fast convolution procedure for processing discrete data requires that a transform of the data and the filter pulse response be formed, followed by the inverse transform of their (complex) product. The finite field fast transform eliminates any roundoff error due to internal multiplication, eliminates truncation of irrational coefficients, and requires only real arithmetic (addition and multiplication). This note develops a realization scheme for such a transform using the Chinese remainder theorem.
9 citations
9 citations
9 citations
TL;DR: The Hartley transform based feature extraction method is proposed for fingerprint matching and reduces the false acceptance rate (FAR) from 21.48% to 16.74 % based on the database of Bologna University and from 31.29% to 28.69%based on the FVC2002 database.
Abstract: The Hartley transform based feature extraction method is proposed for fingerprint matching. Hartley transform is applied on a smaller region that has been cropped around the core point. The performance of this proposed method is evaluated based on the standard database of Bologna University and the database of the FVC2002. We used the city block distance to compute the similarity between the test fingerprint and database fingerprint image. The results obtained are compared with the discrete wavelet transform (DWT) based method. The experimental results show that, the proposed method reduces the false acceptance rate (FAR) from 21.48% to 16.74 % based on the database of Bologna University and from 31.29% to 28.69% based on the FVC2002 database.
9 citations