Optimization of Pattern Recognition of Hijaiyah Letters using Normalized Cross Correlation Tchniques
Sufiatul Maryana1, Iyan Mulyana2, Ema Kurnia3

1Sufiatul Maryana, Department of Information System, Diploma Program Pakuan University, Indonesia.
2Iyan Mulyana, Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan University, Indonesia.
3Ema Kurnia, Department of Information System, Diploma Program Pakuan University, Indonesia.
Manuscript received on 02 August 2019 | Revised Manuscript received on 25 August 2019 | Manuscript Published on 05 September 2019 | PP: 48-53 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10110782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1011.0782S719
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The development of analysis in digital image increasingly developed with various methods, one of which is in recognition of letter patterns. Each letter written using handwriting must have different writing patterns, such as the thickness and shape of the letter pattern. This research will be doing on the pattern recognition of hijaiyah letters of handwriting by applying the Normalized Cross Correlation (NCC) technique. NCC is a technique used to match two images. Before the NCC process, it should be done with the preprocessing using convolution and without convolution using the binary image. The convolution technique used was the Sobel and Prewitt edge detection with the aimed to get the edge of an object and compared the number of matching letters between using edge detection and without edge detection. The tests were done by using the different sized image of 32×32 pixels, 64×64 pixels and then match it against a similar sample data, a different sample data, a different objects font sample data and a different sample data of original image size. The results show that the matching of the letter pattern depends on the size of the image that is more matches to the image of 32×32 pixels. The binary image had better matching numbers than the convolution techniques. While in convolution techniques, Prewitt edge detection had the higher accuracy and matching results compared to the image using Sobel edge detection.
Keywords: Normalized Cross, Correlation Pattern Recognition, Hijaiyah Letters, Convolution, Edge Detection.
Scope of the Article: Pattern Recognition