A comparative study of handwritten character recognition by using Image Processing and Neural Network techniques
Yükleniyor...
Dosyalar
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This 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.
Açıklama
Anahtar Kelimeler
Artificial Intelligence (AI), Edge Detection, Feature Extraction, Gradient, Hidden Layer, Image Correlation, Image Filtering, Noise, Optical Character Recognition (OCR), Pattern Recognition
Kaynak
Hittite Journal of Science and Engineering
WoS Q Değeri
Scopus Q Değeri
Cilt
8
Sayı
2
Künye
Koyuncu, 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/HJSE19030000223
