Comparing the Machine Ability to Recognize Hand-Written Hindu and Arabic Digits

Hindi, Khalil El; Khayyat, Muna; Kar, Areej Abu

doi:10.1080/10798587.2016.1210257

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Much research has been done on printed and handwritten digit recognition using artificial neural network: [10], [12], [13], [18]- [28]. Recently, researchers have begun to pay attention to improve the performance by integration of multiple classifier such as [1], [9], [29]- [37].…”

Section: Related Workmentioning

confidence: 99%

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Multiple Classifiers and Invariant Features Extraction for Digit Recognition

Khedidja¹,

Mouss²

2019

IJCEE

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Extraction of feature and classification methods are important phases in recognition system. A good classifier and extraction of features that suits play a very important role in a recognition system to improving recognition rate. In this paper we propose a new system designed to recognize the ten digits of printed Arabic numerals that are the most common symbolic representation of numbers in the world today. This study has been conducted using Hu moment, number of hole and surface which are tolerate to the geometric transformations along with seven different classifiers Naive Bayesian, Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), Pseudo-Inverse. Support Vector Machine (SVM), Decision Tree and K-Nearest Neighbor (KNN). Classifier combination is considered. Experimental tests demonstrated that our technique achieves good results on multi-font and multi-size printed digit dataset.

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Section: Related Workmentioning

confidence: 99%

“…where g(k,l) is image function and K, M are image dimensions, and are Geometrical central moments of order (i+j) computed using centroid (gravity center) which calculated by (10). are invariants to translation (with image translation to coordinate origin while computing central moments.…”

Section: Feature Extractionmentioning

confidence: 99%

Multiple Classifiers and Invariant Features Extraction for Digit Recognition

Khedidja¹,

Mouss²

2019

IJCEE

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“…Most of such these applications were performed on Arabic digits, because it is the most known numbering system in the world. However, Hindi digits are widely used in some countries in Africa [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Alqudah

Alquran

et al. 2021

Applied Sciences

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Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.

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Comparing the Machine Ability to Recognize Hand-Written Hindu and Arabic Digits

Cited by 2 publications

References 6 publications

Multiple Classifiers and Invariant Features Extraction for Digit Recognition

Multiple Classifiers and Invariant Features Extraction for Digit Recognition

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

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