An efficient multiple classifier system for Arabic handwritten words recognition

Tamen, Zahia; Drias, Habiba; Boughaci, Dalila

doi:10.1016/j.patrec.2017.01.020

Cited by 32 publications

(26 citation statements)

References 26 publications

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“…The classifiers are denoted as , where as shown in Equation (6). The results of 12 classifiers are combining into single combination classifier using two rules [31] with the aim of enhancing the performance of the finger vein recognition system.…”

Section: ) Combination Rulesmentioning

confidence: 99%

Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning

Ali

Kassim

Abdullah

2019

ijacsa

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Human identity recognition and protection of information security are current global concerns in this age of increasing information growth. Biometrics approach of defining identity is considered as one of the highly potential approaches due to its internal feature that is difficult to be artificially recreated, stolen and/or forgotten. The new recognition system based on finger vein is a unique method depending on physiological traits and parameters of the vein patterns for the human. Published works on finger vein identification have hitherto ignored the power of aggregating different types of features and classifiers in improving the performance of the biometric recognition system. In this paper, we developed a novel feature approach named as straight line approximator (SLA) for extending the feature space of vein pattern using a public data set SDUMLA-HMT comprising about 3,816 images of finger vein for 160 persons. Furthermore, we applied a set of extreme learning machine (ELM) and support vector machine (SVM) classifier in different kernels. Then, we used the combination rules to improve the performance of the system. The experiment result of the proposed method achieved an accuracy of 87% using (DS and GWAR) rules at rank 1, while the accuracy of DS rule 93% and GWAR rule 92% at rank 5.

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Section: ) Combination Rulesmentioning

confidence: 99%

Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning

Ali

Kassim

Abdullah

2019

ijacsa

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“…A multiclassifier technique was introduced for Arabic handwritten word recognition [9]. A multiclassifier technique was introduced for Arabic handwritten word recognition [9].…”

Section: Related Workmentioning

confidence: 99%

“…" ligatures, and the overlapping characters correctly. A multiclassifier technique was introduced for Arabic handwritten word recognition [9]. Chebyshev moments (CMs) improved with some statistical and contour-based features (SCF) were used for word images description.…”

Section: Introductionmentioning

confidence: 99%

Enhanced technique for Arabic handwriting recognition using deep belief network and a morphological algorithm for solving ligature segmentation

2018

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Arabic handwriting segmentation and recognition is an area of research that has not yet been fully understood. Dealing with Arabic ligature segmentation, where the Arabic characters are connected and unconstrained naturally, is one of the fundamental problems when dealing with the Arabic script. Arabic character‐recognition techniques consider ligatures as new classes in addition to the classes of the Arabic characters. This paper introduces an enhanced technique for Arabic handwriting recognition using the deep belief network (DBN) and a new morphological algorithm for ligature segmentation. There are two main stages for the implementation of this technique. The first stage involves an enhanced technique of the Sari segmentation algorithm, where a new ligature segmentation algorithm is developed. The second stage involves the Arabic character recognition using DBNs and support vector machines (SVMs). The two stages are tested on the IFN/ENIT and HACDB databases, and the results obtained proved the effectiveness of the proposed algorithm compared with other existing systems.

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“…this section covers the related work on Arabic Handwriting recognition. Hence, a number of Deep learning techniques [1,3,6,8,9] compared with the stat-of-art techniques [2,4]. Nevertheless, all details of works were presented such as, a dataset that used, methods, numerical/characters/word level recognition, and results in accuracy for each research.…”

Section: Deep Learning For Arabic Handwriting Recognitionmentioning

confidence: 99%

“…This language is also used as a method for the transcription other languages such as Turkish, Persian, Kurdish, Urdu, and Malay. Unlike the Chinese and Latin domain, off-line techniques for Arabic Handwritten Recognition (AHWR) are not well-developed yet, because the cursive nature of the language gives rise to numerous technical difficulties (Khémiri, Kacem, & Belaïd, 2014; [2]). HMM-based printed Arabic text recognition is presented by [10] for various scenarios.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms for Arabic Handwriting Recognition: A Review

Al-Saffar¹,

Awang²,

Al-Saiagh³

et al. 2018

IJET

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Computer vision (CV) refers to the study of the computer simulation of human visual science. Major task of CV is to collect images (or video) so that they could be used for analysis, gathering information, and making decisions or judgements. CV has greatly progressed and developed in the past few decades. In recent years, deep learning (DL) approaches have won several contests in pattern recognition and machine learning. (DL) dramatically improved the state-of-the-art in visual object recognition, object detection, handwritten recognition and many other domains. Handwritten recognition technique is one of this tasks that targeted to extract the text from documents or another images type. In contrast to the English domain, there are a limited works on the Arabic language that achieved satisfactory results, Due to the Arabic language cursive nature that induces many technical difficulties. This paper highlighted the pre-processing and binarization methods that have been used in the literature along with proposed numerous directions for developing. We review the various current deep learning approaches and tools used for Arabic handwritten recognition (AHWR), identified challenges along this line of this research, and gives several recommendations including a framework based (DL) that is particularly applicable for dealing with cursive nature languages.

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An efficient multiple classifier system for Arabic handwritten words recognition

Cited by 32 publications

References 26 publications

Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning

Finger Vein Recognition using Straight Line Approximation based on Ensemble Learning

Enhanced technique for Arabic handwriting recognition using deep belief network and a morphological algorithm for solving ligature segmentation

Deep Learning Algorithms for Arabic Handwriting Recognition: A Review

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