A comparative study of handwritten character recognition by using Image Processing and Neural Network techniques

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dc.authorid0000-0002-8444-1094en_US
dc.contributor.authorKoyuncu, Hakan
dc.date.accessioned2022-08-18T13:01:07Z
dc.date.available2022-08-18T13:01:07Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThis study aims to analyze the effects of noise, image filtering, and edge detection techniques in the preprocessing phase of character recognition by using a large set of character images exported from the MNIST database trained with various sizes of neural networks. Canny and Sobel algorithms are deployed to detect the edges of the images. The Canny algorithm can produce smoother and thinner continuous edges compare to the Sobel algorithm. The structural forms were reshaped using the Skeletonization algorithm. The Laplacian filter was used to increase the sharpness of the images and High pass filtering was used to highlight the fine details in blurred images in the form of image filtering. Gaussian noise or image noise with Gaussian intensity was used in Matlab on MNIST character images with the probability density function P. The effects of noise on character images are displayed during character recognition related to Neural network properties. Neural networks are commonly used to recognize patterns among optical characters. Feedforward neural networks are deployed in this study. A comprehensive analysis of the image processing algorithms is included during character recognition. Improved accuracy is observed with character recognition during the prediction phase of the neural networks. A sample of unknown numeric characters is tested with the application of High pass filtering plus feedforward neural network and 89% average output prediction accuracy was obtained against the average number of hidden layers in the neural network. Other prediction accuracies were also tabulated for the reader’s attention.en_US
dc.identifier.citationKoyuncu, H. (2021). A comparative study of handwritten character recognition by using Image Processing and Neural Network techniques. Hittite Journal of Science and Engineering, 8(2), 133 - 140. 10.17350/HJSE19030000223en_US
dc.identifier.endpage140en_US
dc.identifier.issn2148-4171
dc.identifier.issue2en_US
dc.identifier.startpage133en_US
dc.identifier.trdizinid493853
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2937
dc.identifier.volume8en_US
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorKoyuncu, Hakan
dc.language.isoen
dc.relation.ispartofHittite Journal of Science and Engineering
dc.relation.isversionof10.17350/HJSE19030000223en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Intelligence (AI)en_US
dc.subjectEdge Detectionen_US
dc.subjectFeature Extractionen_US
dc.subjectGradienten_US
dc.subjectHidden Layeren_US
dc.subjectImage Correlationen_US
dc.subjectImage Filteringen_US
dc.subjectNoiseen_US
dc.subjectOptical Character Recognition (OCR)en_US
dc.subjectPattern Recognitionen_US
dc.titleA comparative study of handwritten character recognition by using Image Processing and Neural Network techniques
dc.typeArticle

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