Improving the Accuracy for Offline Arabic Digit Recognition Using Sliding Window Approach

Handwritten Digit recognition is a challenging problem these days due to the widely used Arabic language in the world, especially in the Middle East region. In this paper, sliding windows are used to enhance classification accuracies and implemented using random forests (RF) and support vector machi...

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Bibliographic Details
Published in:Iranian journal of science and technology. Transactions of electrical engineering 2020-12, Vol.44 (4), p.1633-1644
Main Authors: Al-wajih, Ebrahim, Ghazali, Rozaida
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Classifiers
Concavity
Discrete cosine transform
Electrical Engineering
Engineering
Feature extraction
Fourier transforms
Handwriting recognition
Histograms
Neural networks
Object recognition
Research Paper
Sliding
Support vector machines
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Summary:Handwritten Digit recognition is a challenging problem these days due to the widely used Arabic language in the world, especially in the Middle East region. In this paper, sliding windows are used to enhance classification accuracies and implemented using random forests (RF) and support vector machine (SVM) classifiers for recognition of Arabic digit images. In order to study their effectiveness with and without using sliding windows, four different feature extraction techniques have been proposed which includes Mean-based, Gray-Level Co-occurrence Matrix (GLCM), Moment-based, and Edge Direction Histogram (EDH). The obtained accuracies show the significance of using sliding windows for classifying digit. The recognition rates acquired using the modified version of AHDBase dataset are 98% when Mean-based and Moment-based are applied with RF classifier, 98.33% and 99.13% when GLCM and EDH are used with linear-kernel SVM, respectively. Moreover, the performance of this study is compared against recent state-of-the-art approaches, namely Geometric-based, two-dimensional discrete cosine transform, Hierarchical features, Hetero-features, Discrete Fourier Transform and geometrical features, Gabor-based, gradient, structural, and concavity and Local Binary Convolutional Neural Networks.
ISSN:2228-6179
2364-1827
DOI:10.1007/s40998-020-00317-5