Deep Morphological Gradient for Recognition of Handwritten Digits

Atillah, Mouhssine El; Fazazy, Khalid El

doi:10.1145/3372938.3372993

Cited by 3 publications

(1 citation statement)

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“…In artificial neural networks (ANN), the activation function serves as a part of an artificial neuron and is in charge of analyzing weighted inputs and assisting in producing an output. When the ReLU is used as the activation function, the function only returns positive values when the input is positive; otherwise, it returns zero (see Eq 5) 19 .…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Classification of Arabic Alphabets Using a Combination of a Convolutional Neural Network and the Morphological Gradient Method

Atillah¹,

Fazazy²,

Riffi³

2023

Baghdad Sci.J

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The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabets are detected using the mathematical algorithm of the morphological gradient. After that, the images are passed to the CNN architecture. The available database of Arabic handwritten alphabets on Kaggle is utilized for examining the model. This database consists of 16,800 images divided into two datasets: 13,440 images for training and 3,360 for validation. As a result, the model gives a remarkable accuracy equal to 99.02%.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%