Arabic handwritten characters recognition system, towards improving its accuracy

Sahlol, Ahmed T.; Elhoseny, Mohamed; Elhariri, Esraa; Hassanien, Aboul Ella

doi:10.1109/itcosp.2017.8303068

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Sahlol et al [12] presented an Arabic OCR system using a number of optimizers. CENPARMI dataset was used for testing of the system using three classifiers Linear Discriminant Analysis (LDA), SVM and Random Forest Trees (RFT).…”

Section: Related Workmentioning

confidence: 99%

Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis

Jehangir¹,

Khan²,

Khan³

et al. 2021

Mehran Univ. res. j. eng. technol.

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This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is developed by collecting handwritten samples from the students of the university on A4 sized page. These collected samples are then scanned, stemmed and preprocessed to form a medium sized database that encompasses 14784 handwritten Pashto character images (336 distinguishing handwritten samples for each 44 characters in Pashto script). Furthermore, the Zernike moments are considered as a feature extractor tool for the proposed OCR system to extract features of each individual character. Linear Discriminant Analysis (LDA) is followed as a recognition tool for the proposed recognition system based on the calculated features map using Zernike moments. Applicability of the proposed system is tested by validating it with 10-fold cross-validation method and an overall accuracy of 63.71% is obtained for the handwritten Pashto isolated characters using the proposed OCR system.

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Section: Related Workmentioning

confidence: 99%

Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis

Jehangir¹,

Khan²,

Khan³

et al. 2021

Mehran Univ. res. j. eng. technol.

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“…Classification work was performed using well-known classifiers, such as K-nearest neighborhood (KNN) [61], multilayer perceptron (MLP) [40], support vector machine (SVM) [41] and linear discriminant analysis (LDA) [42]. These classifiers showed advantages compared to others in relevant image classification works [3], [5], [29], [30] and in other machine learning works [4], [31].…”

Section: Classification Stagementioning

confidence: 99%

Handwritten Arabic Optical Character Recognition Approach Based on Hybrid Whale Optimization Algorithm With Neighborhood Rough Set

et al. 2020

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Accomplishing high recognition performance is considered one of the most important tasks for handwritten Arabic character recognition systems. In general, Optical Character Recognition (OCR) systems are constructed from four phases: pre-processing, feature extraction, feature selection, and classification. Recent literature focused on the selection of appropriate features as a key point towards building a successful and sufficient character recognition system. In this paper, we propose a hybrid machine learning approach that utilizes neighborhood rough sets with a binary whale optimization algorithm to select the most appropriate features for the recognition of handwritten Arabic characters. To validate the proposed approach, we used the CENPARMI dataset, which is a well-known dataset for machine learning experiments involving handwritten Arabic characters. The results show clear advantages of the proposed approach in terms of recognition accuracy, memory footprint, and processor time than those without the features of the proposed method. When comparing the results of the proposed method with other recent state-of-the-art optimization algorithms, the proposed approach outperformed all others in all experiments. Moreover, the proposed approach shows the highest recognition rate with the smallest consumption time compared to deep neural networks such as VGGnet, Resnet, Nasnet, Mobilenet, Inception, and Xception. The proposed approach was also compared with recently published works using the same dataset, which further confirmed the outstanding classification accuracy and time consumption of this approach. The misclassified failure cases were studied and analyzed, which showed that they would likely be confusing for even Arabic natives because the correct interpretation of the characters required the context of their appearance.

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“…Rouini et al [13] presented the use of dynamic random forest classifier based on surf descriptor feature extraction technique. Sahlol et al [14] inspected different classifiers Genetic algorithm (GA), Particl Swam optimization (PSO), Grey Wolf optimization (GWO), and BAT algorithms (BAT) for handwritten Arabic characters recognition. After testing each algorithm, it was concluded that GWO provides prominent results for handwritten Arabic characters recognition.…”

Section: Related Workmentioning

confidence: 99%

KNN and ANN-based Recognition of Handwritten Pashto Letters using Zoning Features

Khan¹,

Ali²,

Ullah³

et al. 2018

ijacsa

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This paper presents a recognition system for handwritten Pashto letters. However, handwritten character recognition is a challenging task. These letters not only differ in shape and style but also vary among individuals. The recognition becomes further daunting due to the lack of standard datasets for inscribed Pashto letters. In this work, we have designed a database of moderate size, which encompasses a total of 4488 images, stemming from 102 distinguishing samples for each of the 44 letters in Pashto. The recognition framework uses zoning feature extractor followed by K-Nearest Neighbour (KNN) and Neural Network (NN) classifiers for classifying individual letter. Based on the evaluation of the proposed system, an overall classification accuracy of approximately 70.05% is achieved by using KNN while 72% is achieved by using NN.

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Arabic handwritten characters recognition system, towards improving its accuracy

Cited by 4 publications

References 12 publications

Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis

Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis

Handwritten Arabic Optical Character Recognition Approach Based on Hybrid Whale Optimization Algorithm With Neighborhood Rough Set

KNN and ANN-based Recognition of Handwritten Pashto Letters using Zoning Features

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