Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule

Salimi, Hamid; Giveki, Davar

doi:10.1007/s10032-012-0195-7

Cited by 23 publications

(7 citation statements)

References 39 publications

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“…By parallel classification methods, two or more kinds of features are integrated to make up a complex vector, on which features are recognized and classified. To a certain extent, both feature fusion technologies can improve classification and recognition performances [10].…”

Section: Feature and Decision Fusionsmentioning

confidence: 99%

A Method for Segmenting Uyghur Symbols

Qi¹

2017

IJPE

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In consideration that Uyghur symbols are poorly recognized and recognition algorithms are impertinent for connecting characters, five kinds of features are extracted from Uyghur symbols after feature analysis and pre-processing, including 8-directional features, fuzzy features and primitive features. According to characteristics of Uyghur, features of Uyghur symbols such as aspect ratio and dynamic speed are extracted. Features are selected by LDA transformation, and the methods for judging relationships among Uyghur primitives are determined. Minimum distance classifier and MQDF classifier are suggested to be used. By recognizing primitives, effectiveness of algorithms is experimentally verified with online Uyghur recognition algorithms of decision fusion strategies by classifiers.

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Section: Feature and Decision Fusionsmentioning

confidence: 99%

A Method for Segmenting Uyghur Symbols

Qi¹

2017

IJPE

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“…Another effort found by Salimi ·and Giveki [17], in which PCA and 2DPCA Ensemble is used on singular value decomposition (SVD). In 2013, Rawdan [18] presented a novel method of centroid distance combination of Rough sets and Artificial Neural Network (RS_RNN) for Arabic/Farsi isolated numeral recognition.…”

Section: Arabic/farsi Digit Recognitionmentioning

confidence: 99%

“…In 2008, Liu and Suen [25] demonstrated six classifiers (MLP neural network, modified quadratic discriminant function (MQDF), discriminative learning quadratic discriminant function (DLQDF), polynomial network classifier (PNC), class-specific feature polynomial classifier (CFPC), and one-versus-all SVM classifier) for both Indian and Arabic handwritten printed numerals (Bangla and Farsi) on databases of ISI Bangla numerals, CENPARMI Farsi numerals (1800) and IFHCDB Farsi numerals (17,740). The gradient direction histogram features were extracted.…”

Section: Arabic/farsi Digit Recognitionmentioning

confidence: 99%

Arabic Script based Digit Recognition Systems

Naz¹,

Ahmed²,

Ahmad³

et al. 2016

Proceedings of the 2015 International Conference on Recent Advances in Computer Systems

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“…Nowadays, the applications of metaheuristic in solving problems are increased dramatically [15][16][17][18]. Among the all metaheuristic algorithms, PSO is a population (swarm) based optimization tool.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Investigation the Effect of Particle Swarm Optimization in Performance of Mixture of Experts

Rani¹,

Hatami²,

Meysamiazad³

2014

IJCA

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Mixture of experts (ME) is one of the most popular and interesting combining methods, which has great potential to improve performance in machine learning. ME is established based on the divide-and-conquer principle in which the problem space is divided between a few neural network experts, supervised by a gating network. In earlier works on ME, different strategies were developed to divide the problem space between the experts. As result, we have introduced a new method based on the principles of Particle Swarm Optimization (PSO) as a learning step in ME. In this paper, different aspects of the proposed method are compared with the common version of ME. The result carried out from this paper shows that the new method is robust to the variation of ensemble complexity in terms of the number of individual experts, and the number of hidden units.

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Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule

Cited by 23 publications

References 39 publications

A Method for Segmenting Uyghur Symbols

A Method for Segmenting Uyghur Symbols

Arabic Script based Digit Recognition Systems

Investigation the Effect of Particle Swarm Optimization in Performance of Mixture of Experts

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